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400 |
Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional
|
https://openreview.net/forum?id=QwoGfQzuMa
|
[
"Sanjeev Raja",
"Martin Sipka",
"Michael Psenka",
"Tobias Kreiman",
"Michal Pavelka",
"Aditi S. Krishnapriyan"
] |
Poster
|
Transition path sampling (TPS), which involves finding probable paths connecting two points on an energy landscape, remains a challenge due to the complexity of real-world atomistic systems. Current machine learning approaches rely on expensive training procedures and under-utilize growing quantities of atomistic data, limiting scalability and generalization. Generative models of atomistic conformational ensembles sample temporally independent states from energy landscapes, but their application to TPS remains mostly unexplored. In this work, we address TPS by interpreting candidate paths as trajectories sampled from stochastic dynamics induced by the learned score function of generative models, namely denoising diffusion and flow matching. Under these dynamics, finding high-likelihood transition paths becomes equivalent to minimizing the Onsager-Machlup (OM) action functional, enabling us to repurpose pre-trained generative models for TPS in a zero-shot fashion. We demonstrate our approach on a Müller-Brown potential and several fast-folding proteins, where we obtain diverse, physically realistic transition pathways, as well as tetrapeptides, where we demonstrate successful TPS on systems not seen by the generative model during training. Our method can be easily incorporated into new generative models, making it practically relevant as models continue to scale and improve.
|
Transition path sampling, molecular dynamics, generative models, diffusion models, flow matching models
|
We repurpose generative models for transition path sampling via minimization of the Onsager-Machlup action functional.
| 14,150 |
2504.18506
|
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] |
https://github.com/ASK-Berkeley/OM-TPS
|
401 |
Long-Term TalkingFace Generation via Motion-Prior Conditional Diffusion Model
|
https://openreview.net/forum?id=aINERD9MzJ
|
[
"Fei Shen",
"Cong Wang",
"Junyao Gao",
"Qin Guo",
"Jisheng Dang",
"Jinhui Tang",
"Tat-Seng Chua"
] |
Poster
|
Recent advances in conditional diffusion models have shown promise for generating realistic TalkingFace videos, yet challenges persist in achieving consistent head movement, synchronized facial expressions, and accurate lip synchronization over extended generations. To address these, we introduce the \textbf{M}otion-priors \textbf{C}onditional \textbf{D}iffusion \textbf{M}odel (\textbf{MCDM}), which utilizes both archived and current clip motion priors to enhance motion prediction and ensure temporal consistency. The model consists of three key elements: (1) an archived-clip motion-prior that incorporates historical frames and a reference frame to preserve identity and context; (2) a present-clip motion-prior diffusion model that captures multimodal causality for accurate predictions of head movements, lip sync, and expressions; and (3) a memory-efficient temporal attention mechanism that mitigates error accumulation by dynamically storing and updating motion features. We also introduce the {TalkingFace-Wild} dataset, a multilingual collection of over 200 hours of footage across 10 languages. Experimental results demonstrate the effectiveness of MCDM in maintaining identity and motion continuity for long-term TalkingFace generation.
|
Diffusion Model, TalkingFace, Pose
| null | 14,128 |
2502.09533
|
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] | null |
402 |
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
|
https://openreview.net/forum?id=6Eg1OrHmg2
|
[
"Aaron J Havens",
"Benjamin Kurt Miller",
"Bing Yan",
"Carles Domingo-Enrich",
"Anuroop Sriram",
"Daniel S. Levine",
"Brandon M Wood",
"Bin Hu",
"Brandon Amos",
"Brian Karrer",
"Xiang Fu",
"Guan-Horng Liu",
"Ricky T. Q. Chen"
] |
Poster
|
We introduce Adjoint Sampling, a highly scalable and efficient algorithm for learning diffusion processes that sample from unnormalized densities, or energy functions. It is the first on-policy approach that allows significantly more gradient updates than the number of energy evaluations and model samples, allowing us to scale to much larger problem settings than previously explored by similar methods.
Our framework is theoretically grounded in stochastic optimal control and shares the same theoretical guarantees as Adjoint Matching, being able to train without the need for corrective measures that push samples towards the target distribution.
We show how to incorporate key symmetries, as well as periodic boundary conditions, for modeling molecules in both cartesian and torsional coordinates.
We demonstrate the effectiveness of our approach through extensive experiments on classical energy functions, and further scale up to neural network-based energy models where we perform amortized conformer generation across many molecular systems.
To encourage further research in developing highly scalable sampling methods, we plan to open source these challenging benchmarks, where successful methods can directly impact progress in computational chemistry. Code \& and benchmarks provided at https://github.com/facebookresearch/adjoint_sampling.
|
Sampling, Stochastic Optimal Control
|
We introduce a highly scalable algorithm for learning to sample from only energy functions, the first of its kind in terms of efficiency, which we scale to new benchmarks on conformer generation.
| 14,121 |
2504.11713
|
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] |
https://github.com/facebookresearch/adjoint_sampling
|
403 |
Confounder-Free Continual Learning via Recursive Feature Normalization
|
https://openreview.net/forum?id=7zrS5hHlfY
|
[
"Yash Shah",
"Camila Gonzalez",
"Mohammad H. Abbasi",
"Qingyu Zhao",
"Kilian M. Pohl",
"Ehsan Adeli"
] |
Poster
|
Confounders are extraneous variables that affect both the input and the target, resulting in spurious correlations and biased predictions. There are recent advances in dealing with or removing confounders in traditional models, such as metadata normalization (MDN), where the distribution of the learned features is adjusted based on the study confounders. However, in the context of continual learning, where a model learns continuously from new data over time without forgetting, learning feature representations that are invariant to confounders remains a significant challenge. To remove their influence from intermediate feature representations, we introduce the Recursive MDN (R-MDN) layer, which can be integrated into any deep learning architecture, including vision transformers, and at any model stage. R-MDN performs statistical regression via the recursive least squares algorithm to maintain and continually update an internal model state with respect to changing distributions of data and confounding variables. Our experiments demonstrate that R-MDN promotes equitable predictions across population groups, both within static learning and across different stages of continual learning, by reducing catastrophic forgetting caused by confounder effects changing over time.
|
deep neural networks, confounders, continual learning, invariant representations, statistical regression
|
We introduce the Recursive Metadata Normalization (R-MDN) layer to learn confounder-invariant feature representations under changing distributions of the data during continual learning.
| 14,097 |
2507.09031
|
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] |
https://github.com/stanfordtailab/RMDN
|
404 |
Reliable and Efficient Amortized Model-based Evaluation
|
https://openreview.net/forum?id=HDbWrsgkB9
|
[
"Sang T. Truong",
"Yuheng Tu",
"Percy Liang",
"Bo Li",
"Sanmi Koyejo"
] |
Poster
|
Comprehensive evaluations of language models (LM) during both development and deployment phases are necessary because these models are thought to possess numerous capabilities as well as safety risks. The average score across a wide range of benchmarks provides a signal that helps guide the use of these LMs in practice. Currently, holistic evaluations are costly due to the large volume of benchmark questions, making frequent evaluations impractical. A popular attempt to lower the cost is to compute the average score on a subset of the benchmark. This approach, unfortunately, often renders an unreliable measure of LM performance because the average score is often confounded with the difficulty of the questions in the benchmark subset. Item response theory (IRT) was designed to address this challenge, providing a reliable measurement by careful controlling for question difficulty. Unfortunately, question difficulty is expensive to estimate. Facing this challenge, we train a model that predicts question difficulty from its content, enabling a reliable measurement at a fraction of the cost. In addition, we leverage this difficulty predictor to further improve the evaluation efficiency through training a question generator given a difficulty level. This question generator is essential in adaptive testing, where, instead of using a random subset of the benchmark questions, informative questions are adaptively chosen based on the current estimation of LLM performance. Experiments on 22 common natural language benchmarks and 183 LMs show that this approach is more reliable and efficient compared to the current common practice.
|
Model Evaluation, Amortization, Adaptive Testing
|
We perform reliable and efficient model-based evaluation by introducing amortized calibration and question generator, validating across 22 NLP benchmarks.
| 14,091 |
2503.13335
|
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] |
https://github.com/sangttruong/reeval
|
405 |
Hybrid Spiking Vision Transformer for Object Detection with Event Cameras
|
https://openreview.net/forum?id=WZKcJZWG3P
|
[
"Qi Xu",
"Jie Deng",
"Jiangrong Shen",
"Biwu Chen",
"Huajin Tang",
"Gang Pan"
] |
Poster
|
Event-based object detection has attracted increasing attention for its high temporal resolution, wide dynamic range, and asynchronous address-event representation. Leveraging these advantages, spiking neural networks (SNNs) have emerged as a promising approach, offering low energy consumption and rich spatiotemporal dynamics. To further enhance the performance of event-based object detection, this study proposes a novel hybrid spike vision Transformer (HsVT) model. The HsVT model integrates a spatial feature extraction module to capture local and global features, and a temporal feature extraction module to model time dependencies and long-term patterns in event sequences. This combination enables HsVT to capture spatiotemporal features, improving its capability in handling complex event-based object detection tasks. To support research in this area, we developed the Fall Detection dataset as a benchmark for event-based object detection tasks. The Fall DVS detection dataset protects facial privacy and reduces memory usage thanks to its event-based representation. Experimental results demonstrate that HsVT outperforms existing SNN methods and achieves competitive performance compared to ANN-based models, with fewer parameters and lower energy consumption.
|
Spiking Neural Networks, Fall Detection, Object Detection With Event Cameras
| null | 14,090 |
2505.07715
|
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] | null |
406 |
PDE-Controller: LLMs for Autoformalization and Reasoning of PDEs
|
https://openreview.net/forum?id=7epYTVsWEI
|
[
"Mauricio Soroco",
"Jialin Song",
"Mengzhou Xia",
"Kye Emond",
"Weiran Sun",
"Wuyang Chen"
] |
Poster
|
We present PDE-Controller, a framework that enables large language models (LLMs) to control systems governed by partial differential equations (PDEs). Traditional LLMs have excelled in commonsense reasoning but fall short in rigorous logical reasoning. While recent AI-for-math has made strides in pure mathematics, areas of applied mathematics, particularly PDEs, remain underexplored despite their significant real-world applications. Our approach enables LLMs to transform informal natural language instructions into formal specifications, and then execute reasoning and planning steps to improve the utility of PDE control. We build a holistic solution comprising datasets (both human-written cases and 2 million synthetic samples), math-reasoning models, and novel evaluation metrics, all of which require significant effort. Our PDE-Controller significantly outperforms the latest open-source and GPT models in reasoning, autoformalization, and program synthesis, achieving up to a 62% improvement in utility gain for PDE control. By bridging the gap between language generation and PDE systems, we demonstrate the potential of LLMs in addressing complex scientific and engineering challenges. We promise to release all data, model checkpoints, and code upon acceptance.
|
AI-for-Math, Large Language Model, Partial Differential Equation
|
We build an LLM termed "PDE-Controller" that can achieve reasoning and planning on PDE (partial differential equation) control problems.
| 14,088 | null |
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] |
https://github.com/delta-lab-ai/pde-controller
|
407 |
Making Hard Problems Easier with Custom Data Distributions and Loss Regularization: A Case Study in Modular Arithmetic
|
https://openreview.net/forum?id=le8hVvWi6Q
|
[
"Eshika Saxena",
"Alberto Alfarano",
"Emily Wenger",
"Kristin E. Lauter"
] |
Poster
|
Recent work showed that ML-based attacks on Learning with Errors (LWE), a hard problem used in post-quantum cryptography, outperform classical algebraic attacks in certain settings. Although promising, ML attacks struggle to scale to more complex LWE settings. Prior work connected this issue to the difficulty of training ML models to do modular arithmetic, a core feature of the LWE problem. To address this, we develop techniques that significantly boost the performance of ML models on modular arithmetic tasks—enabling the models to sum up to $N=128$ elements modulo $q \le 974269$. Our core innovation is the use of custom training data distributions and a carefully designed loss function that better represents the problem structure. We apply an initial proof of concept of our techniques to LWE specifically and find that they allow recovery of 2x harder secrets than prior work. Our techniques also help ML models learn other well-studied problems better, including copy, associative recall, and parity, motivating further study.
|
transformers, modular arithmetic, math, cryptography
|
We introduce two techniques, varying the diversity of training data and introducing a regularized loss function, to improve transformer learning on modular arithmetic, cryptanalysis, and other synthetic tasks
| 14,087 | null |
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] |
https://github.com/facebookresearch/arithmetic
|
408 |
Attention-Level Speculation
|
https://openreview.net/forum?id=4OszSYdsgO
|
[
"Jack Cai",
"Ammar Vora",
"Randolph Zhang",
"Mark O'Connor",
"Mark C. Jeffrey"
] |
Poster
|
As Large Language Models (LLMs) grow in size and context length, efficient inference strategies are essential to maintain low-latency token generation. Unfortunately, conventional tensor and data parallelism face diminishing returns when scaling across multiple devices. We propose a novel form—attention-level speculative parallelism (ALSpec)—that predicts self-attention outputs to execute subsequent operations early on separate devices. Our approach overlaps attention and non-attention computations, reducing the attention latency overhead at 128K context length by up to 5x and improving end-to-end decode latency by up to 1.65x, all without sacrificing quality. We establish the fundamental pillars for speculative execution and provide an execution paradigm that simplifies implementation. We show that existing attention-approximation methods perform well on simple information retrieval tasks, but they fail in advanced reasoning and math. Combined with speculative execution, we can approximate up to 90% of self-attention without harming model correctness. Demonstrated on Tenstorrent's NPU devices, we scale up LLM inference beyond current techniques, paving the way for faster inference in transformer models.
|
Inference, Speculation, Attention, Transformer, Large Language Model
|
We show that attention-level speculation reduces LLM decode latency when tensor parallelism fails to scale.
| 14,085 | null |
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] |
https://github.com/mcj-group/alspec
|
409 |
Prediction-Powered E-Values
|
https://openreview.net/forum?id=rkegUc8d0c
|
[
"Daniel Csillag",
"Claudio Jose Struchiner",
"Guilherme Tegoni Goedert"
] |
Poster
|
Quality statistical inference requires a sufficient amount of data, which can be missing or hard to obtain. To this end, prediction-powered inference has risen as a promising methodology, but existing approaches are largely limited to Z-estimation problems such as inference of means and quantiles. In this paper, we apply ideas of prediction-powered inference to e-values. By doing so, we inherit all the usual benefits of e-values -- such as anytime-validity, post-hoc validity and versatile sequential inference -- as well as greatly expand the set of inferences achievable in a prediction-powered manner. In particular, we show that every inference procedure that can be framed in terms of e-values has a prediction-powered counterpart, given by our method. We showcase the effectiveness of our framework across a wide range of inference tasks, from simple hypothesis testing and confidence intervals to more involved procedures for change-point detection and causal discovery, which were out of reach of previous techniques. Our approach is modular and easily integrable into existing algorithms, making it a compelling choice for practical applications.
|
prediction-powered inference, e-values, statistical inference, distribution-free methods
|
We extend prediction-powered inference to e-values, vastly expanding the set of inference tasks achievable in a prediction-powered manner while also benefiting from the usual virtues of e-values.
| 14,084 | null |
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] |
https://github.com/dccsillag/experiments-prediction-powered-evalues
|
410 |
RAGGED: Towards Informed Design of Scalable and Stable RAG Systems
|
https://openreview.net/forum?id=4ufjBV6S4I
|
[
"Jennifer Hsia",
"Afreen Shaikh",
"Zora Zhiruo Wang",
"Graham Neubig"
] |
Poster
|
Retrieval-augmented generation (RAG) enhances language models by integrating external knowledge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance, making RAG less reliable than closed-book generation. In this work, we introduce RAGGED, a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability. Some readers benefit from increased retrieval depth, while others degrade due to their sensitivity to distracting content. Through large-scale experiments on open-domain, multi-hop, and specialized-domain datasets, we show that retrievers, rerankers, and prompts influence performance but do not fundamentally alter these reader-driven trends. By providing a principled framework and new metrics to assess RAG stability and scalability, RAGGED enables systematic evaluation of retrieval-augmented generation systems, guiding future research on optimizing retrieval depth and model robustness.
|
Retrieval-Augmented Generation, RAG, Evaluation, Information Retrieval, Question Answering
|
We show that retrieval-augmented generation systems depend more on the reader’s ability to handle noise than on retrieval quality, and introduce a framework (RAGGED) to systematically evaluate their stability and scalability.
| 14,057 | null |
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] |
https://github.com/neulab/ragged
|
411 |
H-Tuning: Toward Low-Cost and Efficient ECG-based Cardiovascular Disease Detection with Pre-Trained Models
|
https://openreview.net/forum?id=RLu1QIPiVr
|
[
"Rushuang Zhou",
"Yuanting Zhang",
"Yining Dong"
] |
Poster
|
Fine-tuning large-scale pre-trained models provides an effective solution to alleviate the label scarcity problem in cardiovascular diseases (CVDs) detection using electrocardiogram (ECG). However, as the pre-trained models scale up, the computational costs for fine-tuning and inference become unaffordable on low-level devices deployed for clinical applications. Additionally, maintaining the model performance under low budgets in computational resources remains a significant challenge. However, a comprehensive study that can address them in a joint framework is still lacking. Here, we propose a holistic method (H-Tuning) for low-cost and efficient fine-tuning of pre-trained models on downstream datasets. Then, the inference costs of the models fine-tuned by H-Tuning are further reduced significantly using a knowledge distillation technique. Experiments on four ECG datasets demonstrate that H-Tuning reduces the GPU memory consumption during fine-tuning by 6.34 times while achieving comparable CVDs detection performance to standard fine-tuning. With the knowledge distillation technique, the model inference latency and the memory consumption are reduced by 4.52 times and 19.83 times. As such, the proposed joint framework allows for the utilization of pre-trained models with high computation efficiency and robust performance, exploring a path toward low-cost and efficient CVDs detection. Code is available at https://github.com/KAZABANA/H-Tuning
|
Electrocardiograph, Pre-trained models, Fine-tuning, Cardiovascular diseases.
|
This study allows for the utilization of pre-trained models with high computation efficiency and robust performance, exploring a path toward low-cost and efficient CVDs detection.
| 14,054 | null |
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] |
https://github.com/KAZABANA/H-Tuning
|
412 |
How Do Transformers Learn Variable Binding in Symbolic Programs?
|
https://openreview.net/forum?id=kVtyv7bpnw
|
[
"Yiwei Wu",
"Atticus Geiger",
"Raphaël Millière"
] |
Poster
|
Variable binding---the ability to associate variables with values---is fundamental to symbolic computation and cognition. Although classical architectures typically implement variable binding via addressable memory, it is not well understood how modern neural networks lacking built-in binding operations may acquire this capacity. We investigate this by training a Transformer to dereference queried variables in symbolic programs where variables are assigned either numerical constants or other variables. Each program requires following chains of variable assignments up to four steps deep to find the queried value, and also contains irrelevant chains of assignments acting as distractors. Our analysis reveals a developmental trajectory with three distinct phases during training: (1) random prediction of numerical constants, (2) a shallow heuristic prioritizing early variable assignments, and (3) the emergence of a systematic mechanism for dereferencing assignment chains.
Using causal interventions, we find that the model learns to exploit the residual stream as an addressable memory space, with specialized attention heads routing information across token positions.
This mechanism allows the model to dynamically track variable bindings across layers, resulting in accurate dereferencing.
Our results show how Transformer models can learn to implement systematic variable binding without explicit architectural support, bridging connectionist and symbolic approaches.
|
variable binding, mechanistic interpretability, causal interventions, transformers, language models
|
Developmental interpretability study of a small transformer trained to perform variable binding on programs
| 14,043 |
2505.20896
|
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] | null |
413 |
One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy
|
https://openreview.net/forum?id=pb4om8rWRQ
|
[
"Jiacheng Zhang",
"Benjamin I. P. Rubinstein",
"Jingfeng Zhang",
"Feng Liu"
] |
Poster
|
*Statistical adversarial data detection* (SADD) detects whether an upcoming batch contains *adversarial examples* (AEs) by measuring the distributional discrepancies between *clean examples* (CEs) and AEs. In this paper, we explore the strength of SADD-based methods by theoretically showing that minimizing distributional discrepancy can help reduce the expected loss on AEs. Despite these advantages, SADD-based methods have a potential limitation: they discard inputs that are detected as AEs, leading to the loss of clean information within those inputs. To address this limitation, we propose a two-pronged adversarial defense method, named ***D***istributional-discrepancy-based ***A***dversarial ***D***efense (DAD). In the training phase, DAD first optimizes the test power of the *maximum mean discrepancy* (MMD) to derive MMD-OPT, which is *a stone that kills two birds*. MMD-OPT first serves as a *guiding signal* to minimize the distributional discrepancy between CEs and AEs to train a denoiser. Then, it serves as a *discriminator* to differentiate CEs and AEs during inference. Overall, in the inference stage, DAD consists of a two-pronged process: (1) directly feeding the detected CEs into the classifier, and (2) removing noise from the detected AEs by the distributional-discrepancy-based denoiser. Extensive experiments show that DAD outperforms current *state-of-the-art* (SOTA) defense methods by *simultaneously* improving clean and robust accuracy on CIFAR-10 and ImageNet-1K against adaptive white-box attacks. Codes are publicly available at: https://github.com/tmlr-group/DAD.
|
adversarial defense, adversarial robustness, accuracy-robustness trade-off
| null | 14,032 |
2503.02169
|
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] |
https://github.com/tmlr-group/DAD
|
414 |
Multimodal Medical Code Tokenizer
|
https://openreview.net/forum?id=UaTrcei5Ba
|
[
"Xiaorui Su",
"Shvat Messica",
"Yepeng Huang",
"Ruth Johnson",
"Lukas Fesser",
"Shanghua Gao",
"Faryad Sahneh",
"Marinka Zitnik"
] |
Poster
|
Foundation models trained on patient electronic health records (EHRs) require tokenizing medical data into sequences of discrete vocabulary items. Existing tokenizers treat medical codes from EHRs as isolated textual tokens. However, each medical code is defined by its textual description, its position in ontological hierarchies, and its relationships to other codes, such as disease co-occurrences and drug-treatment associations. Medical vocabularies contain more than 600,000 codes with critical information for clinical reasoning. We introduce MedTok, a multimodal medical code tokenizer that uses the text descriptions and relational context of codes. MedTok processes text using a language model encoder and encodes the relational structure with a graph encoder. It then quantizes both modalities into a unified token space, preserving modality-specific and cross-modality information. We integrate MedTok into five EHR models and evaluate it on operational and clinical tasks across in-patient and out-patient datasets, including outcome prediction, diagnosis classification, drug recommendation, and risk stratification. Swapping standard EHR tokenizers with MedTok improves AUPRC across all EHR models, by 4.10% on MIMIC-III, 4.78% on MIMIC-IV, and 11.32% on EHRShot, with the largest gains in drug recommendation. Beyond EHR modeling, we demonstrate using MedTok tokenizer with medical QA systems. Our results demonstrate the potential of MedTok as a unified tokenizer for medical codes, improving tokenization for medical foundation models.
|
Medical Code, Tokenization, EHR
|
Multimodal Medical Code Tokenizer
| 14,031 |
2502.04397
|
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] | null |
415 |
ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Robotics
|
https://openreview.net/forum?id=grlezgVg4s
|
[
"Letian Chen",
"Nina Marie Moorman",
"Matthew Craig Gombolay"
] |
Poster
|
Reinforcement learning (RL) has demonstrated compelling performance in robotic tasks, but its success often hinges on the design of complex, ad hoc reward functions. Researchers have explored how Large Language Models (LLMs) could enable non-expert users to specify reward functions more easily. However, LLMs struggle to balance the importance of different features, generalize poorly to out-of-distribution robotic tasks, and cannot represent the problem properly with only text-based descriptions. To address these challenges, we propose ELEMENTAL (intEractive LEarning froM dEmoNstraTion And Language), a novel framework that combines natural language guidance with visual user demonstrations to align robot behavior with user intentions better. By incorporating visual inputs, ELEMENTAL overcomes the limitations of text-only task specifications, while leveraging inverse reinforcement learning (IRL) to balance feature weights and match the demonstrated behaviors optimally. ELEMENTAL also introduces an iterative feedback-loop through self-reflection to improve feature, reward, and policy learning. Our experiment results demonstrate that ELEMENTAL outperforms prior work by 42.3% on task success, and achieves 41.3% better generalization in out-of-distribution tasks, highlighting its robustness in LfD.
|
Learning from Demonstration, Vision-Language Models, Inverse Reinforcement Learning
|
ELEMENTAL enables robots to learn user-aligned reward functions by combining user language instructions and demonstration through interactive self-reflection using vision-language models.
| 14,019 |
2411.18825
|
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] | null |
416 |
Tight and Fast Bounds for Multi-Label Learning
|
https://openreview.net/forum?id=rcqVuXU2Gm
|
[
"Yi-Fan Zhang",
"Min-Ling Zhang"
] |
Poster
|
Commonly used evaluation metrics in multi-label learning all involve base loss functions, and the theoretical guarantees of multi-label learning often rely on the properties of base loss functions. Some recent theoretical works have used the Lipschitz continuity of base loss functions to prove the generalization bounds for multi-label learning, but the impact of the smoothness of base loss functions on the generalization bounds is completely unknown. In an attempt to make up for this gap in the generalization theory of multi-label learning, we develop some novel vector-contraction inequalities for smooth base loss functions and derive tight generalization bounds with no dependency on the number of labels, up to logarithmic terms. We then exploit local Rademacher complexity to develop some novel local vector-contraction inequalities for smooth base loss functions, which induce generalization bounds with a tighter dependency on the number of labels and a faster convergence rate with respect to the number of examples. In addition, we derive tight generalization bounds with no dependency on the number of labels, up to logarithmic terms, for Macro-Averaged AUC by exploiting the Lipschitz continuity and smoothness of base loss functions, respectively. Our state-of-the-art theoretical results provide general theoretical guarantees for the generalization of multi-label learning.
|
Multi-Label Learning, Generalization Bound
| null | 14,018 | null |
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] | null |
417 |
LaCache: Ladder-Shaped KV Caching for Efficient Long-Context Modeling of Large Language Models
|
https://openreview.net/forum?id=SDjZtxDo35
|
[
"Dachuan Shi",
"Yonggan Fu",
"Xiangchi Yuan",
"Zhongzhi Yu",
"Haoran You",
"Sixu Li",
"Xin Dong",
"Jan Kautz",
"Pavlo Molchanov",
"Yingyan Celine Lin"
] |
Poster
|
Recent advancements in Large Language Models (LLMs) have spurred interest in numerous applications requiring robust long-range capabilities, essential for processing extensive input contexts and continuously generating extended outputs. As sequence lengths increase, the number of Key-Value (KV) pairs in LLMs escalates, creating a significant efficiency bottleneck.
In this paper, we propose a new KV cache optimization paradigm called LaCache, a training-free method for efficient and accurate generative inference of LLMs. LaCache enables LLMs to simultaneously address both of the critical challenges in long-range modeling: robust long-range capabilities and continuous generation without running out-of-memory (OOM). Specifically, LaCache integrates two key innovations: (1) a ladder-shaped KV cache pattern that stores KV pairs not only sequentially (left-to-right within each layer) but also across layers (from shallow to deep), providing an extended span for capturing long-range dependencies under a fixed storage budget, thereby boosting long-range capabilities; and (2) an iterative compaction mechanism that progressively compresses older caches, freeing up space for new tokens within a fixed cache size. This token distance-based dynamic compression enables more effective continuous generation under constrained cache budgets.
Experiments across various tasks, benchmarks, and LLM models consistently validate LaCache's effectiveness in enhancing LLMs' long-range capabilities. Our code is available at https://github.com/GATECH-EIC/LaCache.
|
KV Cache Storage, Long-Context Modeling, Large Language Models
|
We propose LaCache, a training-free KV cache optimization framework featuring a ladder-shaped storage pattern for accurate and efficient generative inference of LLMs.
| 14,017 | null |
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] |
https://github.com/GATECH-EIC/LaCache
|
418 |
CSG-ODE: ControlSynth Graph ODE For Modeling Complex Evolution of Dynamic Graphs
|
https://openreview.net/forum?id=7hEZd8Rtlh
|
[
"Zhiqiang Wang",
"Xiaoyi Wang",
"Jianqing Liang"
] |
Poster
|
Graph Neural Ordinary Differential Equations (GODE) integrate the Variational Autoencoder (VAE) framework with differential equations, effectively modeling latent space uncertainty and continuous dynamics, excelling in graph data evolution and incompleteness. However, existing GODE face challenges in capturing time-varying relationships and nonlinear node state evolution, which limits their ability to model complex dynamic graphs. To address these issues, we propose the ControlSynth Graph ODE (CSG-ODE). In the VAE encoding phase, CSG-ODE introduces an information transmission-based inter-node importance weighting mechanism, integrating it with latent correlations to guide adaptive graph convolutional recurrent networks for temporal node embedding. During decoding, CSG-ODE employs ODE to model node dynamics, capturing nonlinear evolution through sub-networks with nonlinear activations. For scenarios or prediction tasks that require stability, we extend CSG-ODE to stable CSG-ODE (SCSG-ODE) by constraining weight matrices to learnable anti-symmetric forms, theoretically ensuring enhanced stability. Experiments on traffic, motion capture, and simulated physical systems datasets demonstrate that CSG-ODE outperforms state-of-the-art GODE, while SCSG-ODE achieves both superior performance and optimal stability.
|
Graph Neural Network, Dynamic Graph, Graph Neural ODE, VAE
|
This work introduces ControlSynth Graph ODE (CSG-ODE), a novel approach enhancing dynamic graph modeling via inter-node importance weighting and nonlinear evolution, with its stable extension (SCSG-ODE) achieving superior performance and stability.
| 14,016 | null |
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] | null |
419 |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical Perspective
|
https://openreview.net/forum?id=p6nhzZ9ilZ
|
[
"Junze Deng",
"Qinhang Wu",
"Peizhong Ju",
"Sen Lin",
"Yingbin Liang",
"Ness Shroff"
] |
Poster
|
Rehearsal-based methods have shown superior performance in addressing catastrophic forgetting in continual learning (CL) by storing and training on a subset of past data alongside new data in current task. While such a concurrent rehearsal strategy is widely used, it remains unclear if this approach is always optimal. Inspired by human learning, where sequentially revisiting tasks helps mitigate forgetting, we explore whether sequential rehearsal can offer greater benefits for CL compared to standard concurrent rehearsal. To address this question, we conduct a theoretical analysis of rehearsal-based CL in overparameterized linear models, comparing two strategies: 1) Concurrent Rehearsal, where past and new data are trained together, and 2) Sequential Rehearsal, where new data is trained first, followed by revisiting past data sequentially. By explicitly characterizing forgetting and generalization error, we show that sequential rehearsal performs better when tasks are less similar. These insights further motivate a novel Hybrid Rehearsal method, which trains similar tasks concurrently and revisits dissimilar tasks sequentially. We characterize its forgetting and generalization performance, and our experiments with deep neural networks further confirm that the hybrid approach outperforms standard concurrent rehearsal. This work provides the first comprehensive theoretical analysis of rehearsal-based CL.
|
Continual Learning, rehearsal-based methods, catastrophic forgetting
| null | 14,014 |
2506.00205
|
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] | null |
420 |
Weak-to-Strong Generalization Even in Random Feature Networks, Provably
|
https://openreview.net/forum?id=OUzDIhgiqr
|
[
"Marko Medvedev",
"Kaifeng Lyu",
"Dingli Yu",
"Sanjeev Arora",
"Zhiyuan Li",
"Nathan Srebro"
] |
Poster
|
Weak-to-Strong Generalization (Burns et al.,2024) is the phenomenon whereby a strong student, say GPT-4, learns a task from a weak teacher, say GPT-2, and ends up significantly outperforming the teacher. We show that this phenomenon does not require a complex and pretrained learner like GPT-4, can arise even in simple non-pretrained models, simply due to the size advantage of the student. But, we also show that there are inherint limits to the extent of such weak to strong generalization. We consider students and teachers that are random feature models, described by two-layer networks with a random and fixed bottom layer and trained top layer. A ‘weak’ teacher, with a small number of units (i.e. random features), is trained on the population, and a ‘strong’ student, with a much larger number of units (i.e. random features), is trained only on labels generated by the weak teacher. We demonstrate, prove, and understand how the student can outperform the teacher, even though trained only on data labeled by the teacher. We also explain how such weak-to-strong generalization is enabled by early stopping. We then show the quantitative limits of weak-to-strong generalization in this model, and in fact in a much broader class of models, for arbitrary teacher and student feature spaces and a broad class of learning rules, including when the student features are pre-trained or otherwise more informative. In particular, we show that in such models the student’s
error can only approach zero if the teacher’s error approaches zero, and a strong student cannot “boost” a slightly-better-then-chance teacher to obtain a small error.
|
weak-to-strong, generalization, random features, student-teacher, 2-layer NN, 2-layer networks
| null | 14,003 |
2503.02877
|
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] | null |
421 |
Identification of Latent Confounders via Investigating the Tensor Ranks of the Nonlinear Observations
|
https://openreview.net/forum?id=WH3ZRH2jno
|
[
"Zhengming Chen",
"Yewei Xia",
"Feng Xie",
"Jie Qiao",
"Zhifeng Hao",
"Ruichu Cai",
"Kun Zhang"
] |
Poster
|
We study the problem of learning discrete latent variable causal structures from mixed-type observational data. Traditional methods, such as those based on the tensor rank condition, are designed to identify discrete latent structure models and provide robust identification bounds for discrete causal models. However, when observed variables—specifically, those representing the children of latent variables—are collected at various levels with continuous data types, the tensor rank condition is not applicable, limiting further causal structure learning for latent variables. In this paper, we consider a more general case where observed variables can be either continuous or discrete, and further allow for scenarios where multiple latent parents cause the same set of observed variables. We show that, under the completeness condition, it is possible to discretize the data in a way that satisfies the full-rank assumption required by the tensor rank condition. This enables the identifiability of discrete latent structure models within mixed-type observational data.
Moreover, we introduce the two-sufficient measurement condition, a more general structural assumption under which the tensor rank condition holds and the underlying latent causal structure is identifiable by a proposed two-stage identification algorithm. Extensive experiments on both simulated and real-world data validate the effectiveness of our method.
|
Causal discovery, discrete latent variables
|
We study the problem of learning causal structure of discrete latent variables
| 14,001 | null |
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] | null |
422 |
Disentangling and Integrating Relational and Sensory Information in Transformer Architectures
|
https://openreview.net/forum?id=lbrqeIipJr
|
[
"Awni Altabaa",
"John Lafferty"
] |
Poster
|
Relational reasoning is a central component of generally intelligent systems, enabling robust and data-efficient inductive generalization. Recent empirical evidence shows that many existing neural architectures, including Transformers, struggle with tasks requiring relational reasoning. In this work, we distinguish between two types of information: *sensory* information about the properties of individual objects, and *relational* information about the relationships between objects. While neural attention provides a powerful mechanism for controlling the flow of sensory information between objects, the Transformer lacks an explicit computational mechanism for routing and processing relational information. To address this limitation, we propose an architectural extension of the Transformer framework that we call the *Dual Attention Transformer (DAT)*, featuring two distinct attention mechanisms: sensory attention for directing the flow of sensory information, and a novel relational attention mechanism for directing the flow of relational information. We empirically evaluate *DAT* on a diverse set of tasks ranging from synthetic relational benchmarks to complex real-world tasks such as language modeling and visual processing. Our results demonstrate that integrating explicit relational computational mechanisms into the Transformer architecture leads to significant performance gains in terms of data efficiency and parameter efficiency.
|
relational, reasoning, attention, transformers, inductive biases, sensory, relational, architecture, attention
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We introduce an extension of the Transformer architecture with explicit relational computational mechanisms, integrating sensory and relational processing.
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2405.16727
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] |
https://github.com/awni00/dual-attention
|
423 |
Sample-specific Noise Injection for Diffusion-based Adversarial Purification
|
https://openreview.net/forum?id=6nbcwJVZNy
|
[
"Yuhao Sun",
"Jiacheng Zhang",
"Zesheng Ye",
"Chaowei Xiao",
"Feng Liu"
] |
Poster
|
*Diffusion-based purification* (DBP) methods aim to remove adversarial noise from the input sample by first injecting Gaussian noise through a forward diffusion process, and then recovering the clean example through a reverse generative process. In the above process, how much Gaussian noise is injected to the input sample is key to the success of DBP methods, which is controlled by a constant noise level $t*$ for all samples in existing methods. In this paper, we discover that an optimal $t*$ for each sample indeed could be different. Intuitively, the cleaner a sample is, the less the noise it should be injected, and vice versa. Motivated by this finding, we propose a new framework, called Sample-specific Score-aware Noise Injection (SSNI). Specifically, SSNI uses a pre-trained score network to estimate how much a data point deviates from the clean data distribution (i.e., score norms). Then, based on the magnitude of score norms, SSNI applies a reweighting function to adaptively adjust $t*$ for each sample, achieving sample-specific noise injections. Empirically, incorporating our framework with existing DBP methods results in a notable improvement in both accuracy and robustness on CIFAR-10 and ImageNet-1K, highlighting the necessity to allocate *distinct noise levels to different samples* in DBP methods. Our code is available at: https://github.com/tmlr-group/SSNI.
|
adversarial purification, adversarial robustness, diffusion-based adversarial purification, accuracy-robustness trade-of
| null | 13,990 |
2506.06027
|
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] |
https://github.com/tmlr-group/SSNI
|
424 |
NestQuant: nested lattice quantization for matrix products and LLMs
|
https://openreview.net/forum?id=4OWGON33HE
|
[
"Semyon Savkin",
"Eitan Porat",
"Or Ordentlich",
"Yury Polyanskiy"
] |
Poster
|
Post-training quantization (PTQ) has emerged as a critical technique for efficient deployment of large language models (LLMs). This work proposes NestQuant, a novel PTQ scheme for weights and activations that is based on self-similar nested lattices. Recent works have mathematically shown such quantizers to be information-theoretically optimal for low-precision matrix multiplication. We implement a practical low-complexity version of NestQuant based on Gosset lattice, making it a drop-in quantizer for any matrix multiplication step (e.g., in self-attention, MLP etc). For example, NestQuant quantizes weights, KV-cache, and activations of Llama-3-8B to 4 bits, achieving perplexity of 6.6 on wikitext2. This represents more than 55\% reduction in perplexity gap with respect to unquantized model (perplexity of 6.14) compared to state-of-the-art Meta's SpinQuant (perplexity 7.3), OstQuant (7.3) and QuaRot (8.2). Comparisons on bigger models (up to 70B) and on various LLM evaluation benchmarks confirm uniform superiority of NestQuant.
|
large language models, quantization
|
Nested lattice codes for LLM quantization
| 13,986 |
2502.09720
|
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] |
https://github.com/cookiedoth/nestquant
|
425 |
Interpolating Neural Network-Tensor Decomposition (INN-TD): a scalable and interpretable approach for large-scale physics-based problems
|
https://openreview.net/forum?id=xfWYB81p5O
|
[
"Jiachen Guo",
"Xiaoyu Xie",
"Chanwook Park",
"Hantao Zhang",
"Matthew J. Politis",
"Gino Domel",
"Wing Kam Liu"
] |
Poster
|
Deep learning has been extensively employed as a powerful function approximator for modeling physics-based problems described by partial differential equations (PDEs). Despite their popularity, standard deep learning models often demand prohibitively large computational resources and yield limited accuracy when scaling to large-scale, high-dimensional physical problems. Their black-box nature further hinders their application in industrial problems where interpretability and high precision are critical. To overcome these challenges, this paper introduces Interpolating Neural Network-Tensor Decomposition (INN-TD), a scalable and interpretable framework that has the merits of both machine learning and finite element methods for modeling large-scale physical systems. By integrating locally supported interpolation functions from finite element into the network architecture, INN-TD achieves a sparse learning structure with enhanced accuracy, faster training/solving speed, and reduced memory footprint. This makes it particularly effective for tackling large-scale high-dimensional parametric PDEs in training, solving, and inverse optimization tasks in physical problems where high precision is required.
|
Scientific Machine Learning; Large-scale simulation; Partial Differential Equations (PDEs); Numerical Analysis; Tensor decomposition
|
Novel neural networks based on interpolation theory from numerical analysis that has faster training/solving speed and better accuracy for modeling high-dimensional large-scale physics-based PDEs
| 13,975 |
2503.02041
|
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] | null |
426 |
MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters
|
https://openreview.net/forum?id=H78W6bTkuZ
|
[
"Arsalan Sharifnassab",
"Saber Salehkaleybar",
"Richard S. Sutton"
] |
Poster
|
We address the challenge of optimizing meta-parameters (hyperparameters) in machine learning, a key factor for efficient training and high model performance. Rather than relying on expensive meta-parameter search methods, we introduce MetaOptimize: a dynamic approach that adjusts meta-parameters, particularly step sizes (also known as learning rates), during training. More specifically, MetaOptimize can wrap around any first-order optimization algorithm, tuning step sizes on the fly to minimize a specific form of regret that considers the long-term impact of step sizes on training, through a discounted sum of future losses. We also introduce lower-complexity variants of MetaOptimize that, in conjunction with its adaptability to various optimization algorithms, achieve performance comparable to those of the best hand-crafted learning rate schedules across diverse machine learning tasks.
|
Continual optimization, meta-parameter optimization
| null | 13,965 |
2402.02342
|
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] |
https://github.com/sabersalehk/MetaOptimize
|
427 |
Calibrating Video Watch-time Predictions with Credible Prototype Alignment
|
https://openreview.net/forum?id=aYUjVw9zcO
|
[
"Chao Cui",
"Shisong Tang",
"Fan Li",
"Jiechao Gao",
"Hechang Chen"
] |
Poster
|
Accurately predicting user watch-time is crucial for enhancing user stickiness and retention in video recommendation systems. Existing watch-time prediction approaches typically involve transformations of watch-time labels for prediction and subsequent reversal, ignoring both the natural distribution properties of label and the \textit{instance representation confusion} that results in inaccurate predictions. In this paper, we propose ProWTP, a two-stage method combining prototype learning and optimal transport for watch-time regression prediction, suitable for any deep recommendation model. Specifically, we observe that the watch-ratio (the ratio of watch-time to video duration) within the same duration bucket exhibits a multimodal distribution. To facilitate incorporation into models, we use a hierarchical vector quantised variational autoencoder (HVQ-VAE) to convert the continuous label distribution into a high-dimensional discrete distribution, serving as credible prototypes for calibrations. Based on this, ProWTP views the alignment between prototypes and instance representations as a Semi-relaxed Unbalanced Optimal Transport (SUOT) problem, where the marginal constraints of prototypes are relaxed. And the corresponding optimization problem is reformulated as a weighted Lasso problem for solution. Moreover, ProWTP introduces the assignment and compactness losses to encourage instances to cluster closely around their respective prototypes, thereby enhancing the prototype-level distinguishability. Finally, we conducted extensive offline experiments on two industrial datasets, demonstrating our consistent superiority in real-world application.
|
Prototype learning, optimal transport, recommendation
| null | 13,964 | null |
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] | null |
428 |
Auditing Prompt Caching in Language Model APIs
|
https://openreview.net/forum?id=gUj2fxQcLZ
|
[
"Chenchen Gu",
"Xiang Lisa Li",
"Rohith Kuditipudi",
"Percy Liang",
"Tatsunori Hashimoto"
] |
Poster
|
Prompt caching in large language models (LLMs) results in data-dependent timing variations: cached prompts are processed faster than non-cached prompts. These timing differences introduce the risk of side-channel timing attacks. For example, if the cache is shared across users, an attacker could identify cached prompts from fast API response times to learn information about other users' prompts. Because prompt caching may cause privacy leakage, transparency around the caching policies of API providers is important. To this end, we develop and conduct statistical audits to detect prompt caching in real-world LLM API providers. We detect global cache sharing across users in seven API providers, including OpenAI, resulting in potential privacy leakage about users' prompts. Timing variations due to prompt caching can also result in leakage of information about model architecture. Namely, we find evidence that OpenAI's embedding model is a decoder-only Transformer, which was previously not publicly known.
|
audit, prompt caching, privacy, transparency, large language models
|
We develop and conduct statistical audits to detect prompt caching and cache sharing in real-world language model APIs, as prompt caching may cause privacy leakage.
| 13,956 |
2502.07776
|
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] |
https://github.com/chenchenygu/auditing-prompt-caching
|
429 |
Tensor Product Neural Networks for Functional ANOVA Model
|
https://openreview.net/forum?id=Ci3nWnys6T
|
[
"Seokhun Park",
"Insung Kong",
"yongchan Choi",
"Chanmoo Park",
"Yongdai Kim"
] |
Poster
|
Interpretability for machine learning models is becoming more and more important as machine learning models become more complex.
The functional ANOVA model, which decomposes a high-dimensional function into a sum of lower dimensional functions (commonly referred to as components), is one of the most popular tools for interpretable AI, and recently, various neural networks have been developed for estimating each component in the functional ANOVA model.
However, such neural networks are highly unstable when estimating each component since the components themselves are not uniquely defined.
That is, there are multiple functional ANOVA decompositions for a given function.
In this paper, we propose a novel neural network which guarantees a unique functional ANOVA decomposition and thus is able to estimate each component stably.
We call our proposed neural network ANOVA Tensor Product Neural Network (ANOVA-TPNN) since
it is motivated by the tensor product basis expansion.
Theoretically, we prove that ANOVA-TPNN can approximate any smooth function well.
Empirically, we show that ANOVA-TPNN provide much more stable estimation of each component and thus much more stable interpretation when training data and initial values of the model parameters vary than existing neural networks do.
Our source code is released at https://github.com/ParkSeokhun/ANOVA-TPNN
|
Interpretability, Trustworthy AI, Functional ANOVA model, Generalized additive models, Tensor product neural network
|
In this paper, we propose a novel neural network which guarantees a unique functional ANOVA decomposition and thus is able to estimate each component stably and accurately.
| 13,951 |
2502.15215
|
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] |
https://github.com/ParkSeokhun/ANOVA-TPNN
|
430 |
Instruction-Following Pruning for Large Language Models
|
https://openreview.net/forum?id=juARG7yu4P
|
[
"Bairu Hou",
"Qibin Chen",
"Jianyu Wang",
"Guoli Yin",
"Chong Wang",
"Nan Du",
"Ruoming Pang",
"Shiyu Chang",
"Tao Lei"
] |
Poster
|
With the rapid scaling of large language models (LLMs), structured pruning has become a widely used technique to learn efficient, smaller models from larger ones, delivering superior performance compared to training similarly sized models from scratch. In this paper, we move beyond the traditional static pruning approach of determining a fixed pruning mask for a model, and propose a dynamic approach to structured pruning. In our method, the pruning mask is input-dependent and adapts dynamically based on the information described in a user instruction. Our approach, termed "instruction-following pruning'', introduces a sparse mask predictor that takes the user instruction as input and dynamically selects the most relevant model parameters for the given task. To identify and activate effective parameters, we jointly optimize the sparse mask predictor and the LLM, leveraging both instruction-following data and the pre-training corpus. Experimental results demonstrate the effectiveness of our approach on a wide range of evaluation benchmarks. For example, our 3B activated model improves over the 3B dense model by 5-8 points of absolute margin on domains such as math and coding, and rivals the performance of a 9B model.
|
Large language model, model pruning, contextual sparsity, pre-training, fine-tuning
| null | 13,949 |
2501.02086
|
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] | null |
431 |
Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilities
|
https://openreview.net/forum?id=xWu5qpDK6U
|
[
"Sreyan Ghosh",
"Zhifeng Kong",
"Sonal Kumar",
"S Sakshi",
"Jaehyeon Kim",
"Wei Ping",
"Rafael Valle",
"Dinesh Manocha",
"Bryan Catanzaro"
] |
Poster
|
Understanding and reasoning over non-speech sounds and music are crucial for both humans and AI agents to interact effectively with their environments. In this paper, we introduce Audio Flamingo 2 (AF2), an Audio-Language Model (ALM) with advanced audio understanding and reasoning capabilities. AF2 leverages (i) a custom CLAP model, (ii) synthetic Audio QA data for fine-grained audio reasoning, and (iii) a multi-stage curriculum learning strategy. AF2 achieves state-of-the-art performance with only a 3B parameter small language model, surpassing large open-source and proprietary models across over 20 benchmarks. Next, for the first time, we extend audio understanding to long audio segments (30 secs to 5 mins) and propose LongAudio, a large and novel dataset for training ALMs on long audio captioning and question-answering tasks. Fine-tuning AF2 on LongAudio leads to exceptional performance on our proposed LongAudioBench, an expert-annotated benchmark for evaluating ALMs on long audio understanding capabilities. We conduct extensive ablation studies to confirm the efficacy of our approach.
|
audio, sound, large audio-language model
|
A parameter-efficient Audio Language Model that achieves sota on several audio understanding and reasoning benchmarks.
| 13,946 |
2503.03983
|
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] | null |
432 |
It's My Data Too: Private ML for Datasets with Multi-User Training Examples
|
https://openreview.net/forum?id=8bGEHOTvmq
|
[
"Arun Ganesh",
"Ryan McKenna",
"Hugh Brendan McMahan",
"Adam Smith",
"Fan Wu"
] |
Poster
|
We initiate a study of algorithms for model training with user-level differential privacy (DP), where each example may be attributed to multiple users, which we call the multi-attribution model. We first provide a carefully chosen definition of user-level DP under the multi-attribution model. Training in the multi-attribution model is facilitated by solving the contribution bounding problem, i.e. the problem of selecting a subset of the dataset for which each user is associated with a limited number of examples. We propose a greedy baseline algorithm for the contribution bounding problem. We then empirically study this algorithm for a synthetic logistic regression task and a transformer training task, including studying variants of this baseline algorithm that optimize the subset chosen using different techniques and criteria. We find that the baseline algorithm remains competitive with its variants in most settings, and build a better understanding of the practical importance of a bias-variance tradeoff inherent in solutions to the contribution bounding problem.
|
differential privacy, multi-attribution
|
We initiate a study of algorithms for model training with user-level differential privacy (DP), where each example is associated with multiple users
| 13,939 |
2503.03622
|
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] | null |
433 |
Understanding the Kronecker Matrix-Vector Complexity of Linear Algebra
|
https://openreview.net/forum?id=2qTwKMDAsD
|
[
"Raphael A Meyer",
"William Joseph Swartworth",
"David Woodruff"
] |
Poster
|
We study the computational model where we can access a matrix $\mathbf{A}$ only by computing matrix-vector products $\mathbf{A}\mathrm{x}$ for vectors of the form $\mathrm{x} = \mathrm{x}_1 \otimes \cdots \otimes \mathrm{x}_q$.
We prove exponential lower bounds on the number of queries needed to estimate various properties, including the trace and the top eigenvalue of $\mathbf{A}$.
Our proofs hold for all adaptive algorithms, modulo a mild conditioning assumption on the algorithm's queries.
We further prove that algorithms whose queries come from a small alphabet (e.g., $\mathrm{x}_i \in \\{\pm1\\}^n$) cannot test if $\mathbf{A}$ is identically zero with polynomial complexity, despite the fact that a single query using Gaussian vectors solves the problem with probability 1.
In steep contrast to the non-Kronecker case, this shows that sketching $\mathbf{A}$ with different distributions of the same subguassian norm can yield exponentially different query complexities.
Our proofs follow from the observation that random vectors with Kronecker structure have exponentially smaller inner products than their non-Kronecker counterparts.
|
Tensors, Kronecker measurements, Sketching, Matrix-Vector, Lower Bound, Query Complexity
|
First exponential lower bounds on the number of Kronecker matrix-vector products to solve Linear Algebra Problems
| 13,930 |
2502.08029
|
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] | null |
434 |
Fair Clustering via Alignment
|
https://openreview.net/forum?id=jImlK83NmV
|
[
"Kunwoong Kim",
"Jihu Lee",
"Sangchul Park",
"Yongdai Kim"
] |
Poster
|
Algorithmic fairness in clustering aims to balance the proportions of instances assigned to each cluster with respect to a given sensitive attribute.
While recently developed fair clustering algorithms optimize clustering objectives under specific fairness constraints, their inherent complexity or approximation often results in suboptimal clustering utility or numerical instability in practice.
To resolve these limitations, we propose a new fair clustering algorithm based on a novel decomposition of the fair $K$-means clustering objective function.
The proposed algorithm, called Fair Clustering via Alignment (FCA), operates by alternately (i) finding a joint probability distribution to align the data from different protected groups, and (ii) optimizing cluster centers in the aligned space.
A key advantage of FCA is that it theoretically guarantees approximately optimal clustering utility for any given fairness level without complex constraints, thereby enabling high-utility fair clustering in practice.
Experiments show that FCA outperforms existing methods by (i) attaining a superior trade-off between fairness level and clustering utility, and (ii) achieving near-perfect fairness without numerical instability.
|
Clustering, Fairness, Trustworthy AI
|
This paper proposes a new algorithm for fair clustering based on a novel decomposition of the fair clustering objective, achieving an approximately optimal trade-off between fairness level and clustering utility for any given fairness level.
| 13,919 |
2505.09131
|
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] |
https://github.com/kwkimonline/FCA
|
435 |
Computing Voting Rules with Improvement Feedback
|
https://openreview.net/forum?id=VFI7HottBp
|
[
"Evi Micha",
"Vasilis Varsamis"
] |
Poster
|
Aggregating preferences under incomplete or constrained feedback is a fundamental problem in social choice and related domains. While prior work has established strong impossibility results for pairwise comparisons, this paper extends the inquiry to improvement feedback, where voters express incremental adjustments rather than complete preferences. We provide a complete characterization of the positional scoring rules that can be computed given improvement feedback. Interestingly, while plurality is learnable under improvement feedback—unlike with pairwise feedback—strong impossibility results persist for many other positional scoring rules. Furthermore, we show that improvement feedback, unlike pairwise feedback, does not suffice for the computation of any Condorcet-consistent rule. We complement our theoretical findings with experimental results, providing further insights into the practical implications of improvement feedback for preference aggregation.
|
Preference Aggregation, Incomplete Preferences, Improvement Feedback, Positional Scoring Rules, Condorcet-Consistent Rules, Computational Social Choice
|
This paper studies the limits of learning social choice functions using t-improvement feedback.
| 13,914 |
2502.12542
|
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] |
https://github.com/VasilisVar00/Computing-Voting-Rules-with-Improvement-Feedback
|
436 |
Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models
|
https://openreview.net/forum?id=19kqoNoc2N
|
[
"Yuan Li",
"Zhengzhong Liu",
"Eric P. Xing"
] |
Poster
|
Optimizing data mixtures for supervised fine-tuning (SFT) of large language models (LLMs) is critical for developing general-purpose models, yet this area remains underexplored. In this paper, we frame data mixing as an optimization problem and introduce a novel method designed to minimize validation loss. Our approach parametrizes the loss by modeling effective data transferred and leveraging scaling laws for fine-tuning. By experimenting with various small-scale data mixtures, we fit these parameters and derive the optimal weights. We provide both mathematical proofs and empirical results demonstrating that our algorithm achieves excellent overall and individual performance across all domains. Through controlled experiments, we show that models trained with our optimized weights perform on par with those using optimal weights determined via grid search, with per-domain loss only 0.66\% higher than the best domain loss from grid search on average. Additionally, we show that reweighting popular SFT datasets using our method improves both validation loss and downstream performance. Finally, we discuss how our method can generalize to guide data selection for domain-specific models and provide insights into SFT.
|
Large language models, supervised fine-tuning, data mixing
| null | 13,907 | null |
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] | null |
437 |
Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction
|
https://openreview.net/forum?id=GZ7gwOZ6Or
|
[
"Ruben Weitzman",
"Peter Mørch Groth",
"Lood Van Niekerk",
"Aoi Otani",
"Yarin Gal",
"Debora Susan Marks",
"Pascal Notin"
] |
Poster
|
Retrieving homologous protein sequences is essential for a broad range of protein modeling tasks such as fitness prediction, protein design, structure modeling, and protein-protein interactions. Traditional workflows have relied on a two-step process: first retrieving homologs via Multiple Sequence Alignments (MSA), then training mod- els on one or more of these alignments. However, MSA-based retrieval is computationally expensive, struggles with highly divergent sequences or complex insertions & deletions patterns, and operates independently of the downstream modeling objective. We introduce Protriever, an end-to-end differentiable framework that learns to retrieve relevant homologs while simultaneously training for the target task. When applied to protein fitness prediction, Protriever achieves state-of-the-art performance compared to sequence-based models that rely on MSA-based homolog retrieval, while being two orders of magnitude faster through efficient vector search. Protriever is both architecture and task-agnostic, and can flexibly adapt to different retrieval strategies and protein databases at inference time – offering a scalable alternative to alignment-centric approaches.
|
Computational Biology
| null | 13,905 |
2506.08954
|
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] |
https://github.com/OATML-Markslab/Protriever
|
438 |
Settling the Maximin Share Fairness for Scheduling among Groups of Machines
|
https://openreview.net/forum?id=Z4I9cf7WY6
|
[
"Bo Li",
"Fangxiao Wang",
"Xing Shiji"
] |
Poster
|
We study the fair scheduling of jobs among groups of (unrelated) machines and focus on the maximin share (MMS) fairness at the group level.
The problem was first introduced by Li et al. [NeurIPS 2023], where each group consists of a number of identical machines (or identical up to different speeds), and the cost of a group is determined by the minimum makespan on completing all jobs assigned to it.
It is left as an open problem when the machines within each group are unrelated.
In this paper, we first resolve this problem and design a polynomial-time algorithm that computes a 2-approximate MMS allocation via linear programming techniques.
We complement this result with a hard instance, showing that no algorithm can be better than $(2-\frac{1}{n})$-approximate MMS, where $n$ is the number of machines.
Thus the approximation ratio 2 is asymptotically tight.
When the groups consist of identical machines, we improve the approximation ratio to $\frac{4}{3}$.
|
Fair division, Maximin share, Job scheduling
|
The paper studies the fair scheduling of jobs among groups of (unrelated) machines and focus on the maximin share (MMS) fairness at the group level.
| 13,902 | null |
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] | null |
439 |
When can in-context learning generalize out of task distribution?
|
https://openreview.net/forum?id=YKyza9lrv4
|
[
"Chase Goddard",
"Lindsay M. Smith",
"Vudtiwat Ngampruetikorn",
"David J. Schwab"
] |
Poster
|
In-context learning (ICL) is a remarkable capability of pretrained transformers that allows models to generalize to unseen tasks after seeing only a few examples. We investigate empirically the conditions necessary on the pretraining distribution for ICL to emerge and generalize \emph{out-of-distribution}. Previous work has focused on the number of distinct tasks necessary in the pretraining dataset. Here, we use a different notion of task diversity to study the emergence of ICL in transformers trained on linear functions. We find that as task diversity increases, transformers undergo a transition from a specialized solution, which exhibits ICL only within the pretraining task distribution, to a solution which generalizes out of distribution to the entire task space. We also investigate the nature of the solutions learned by the transformer on both sides of the transition, and observe similar transitions in nonlinear regression problems. We construct a phase diagram to characterize how our concept of task diversity interacts with the number of pretraining tasks. In addition, we explore how factors such as the depth of the model and the dimensionality of the regression problem influence the transition.
|
In-context learning, Generalization, OOD, out-of-distribution, Machine Learning, ICL
|
We observe a transition between transformers that learn a specialized solution and a generalizing solution when trained to do in-context learning of linear functions with varying task diversity.
| 13,899 |
2506.05574
|
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] | null |
440 |
Bayesian Inference for Correlated Human Experts and Classifiers
|
https://openreview.net/forum?id=sw2pUzbTf1
|
[
"Markelle Kelly",
"Alex James Boyd",
"Sam Showalter",
"Mark Steyvers",
"Padhraic Smyth"
] |
Poster
|
Applications of machine learning often involve making predictions based on both model outputs and the opinions of human experts. In this context, we investigate the problem of querying experts for class label predictions, using as few human queries as possible, and leveraging the class probability estimates of pre-trained classifiers. We develop a general Bayesian framework for this problem, modeling expert correlation via a joint latent representation, enabling simulation-based inference about the utility of additional expert queries, as well as inference of posterior distributions over unobserved expert labels. We apply our approach to two real-world medical classification problems, as well as to CIFAR-10H and ImageNet-16H, demonstrating substantial reductions relative to baselines in the cost of querying human experts while maintaining high prediction accuracy.
|
human-ai, consensus, bayesian
| null | 13,891 |
2506.05636
|
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] | null |
441 |
SEFE: Superficial and Essential Forgetting Eliminator for Multimodal Continual Instruction Tuning
|
https://openreview.net/forum?id=teJdFzLnKh
|
[
"Jinpeng Chen",
"Runmin Cong",
"Yuzhi Zhao",
"Hongzheng Yang",
"Guangneng Hu",
"Horace Ip",
"Sam Kwong"
] |
Poster
|
Multimodal Continual Instruction Tuning (MCIT) aims to enable Multimodal Large Language Models (MLLMs) to incrementally learn new tasks without catastrophic forgetting, thus adapting to evolving requirements. In this paper, we explore the forgetting caused by such incremental training, categorizing it into superficial forgetting and essential forgetting. Superficial forgetting refers to cases where the model’s knowledge may not be genuinely lost, but its responses to previous tasks deviate from expected formats due to the influence of subsequent tasks’ answer styles, making the results unusable. On the other hand, essential forgetting refers to situations where the model provides correctly formatted but factually inaccurate answers, indicating a true loss of knowledge. Assessing essential forgetting necessitates addressing superficial forgetting first, as severe superficial forgetting can conceal the model’s knowledge state. Hence, we first introduce the Answer Style Diversification (ASD) paradigm, which defines a standardized process for data style transformations across different tasks, unifying their training sets into similarly diversified styles to prevent superficial forgetting caused by style shifts. Building on this, we propose RegLoRA to mitigate essential forgetting. RegLoRA stabilizes key parameters where prior knowledge is primarily stored by applying regularization to LoRA’s weight update matrices, enabling the model to retain existing competencies while remaining adaptable to new tasks. Experimental results demonstrate that our overall method, SEFE, achieves state-of-the-art performance.
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Multimodal Continual Instruction Tuning, Multimodal Large Language Model, Continual Learning
| null | 13,874 |
2505.02486
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] |
https://github.com/jinpeng0528/SEFE
|
442 |
Structured Preconditioners in Adaptive Optimization: A Unified Analysis
|
https://openreview.net/forum?id=GzS6b5Xvvu
|
[
"Shuo Xie",
"Tianhao Wang",
"Sashank J. Reddi",
"Sanjiv Kumar",
"Zhiyuan Li"
] |
Poster
|
We present a novel unified analysis for a broad class of adaptive optimization algorithms with structured (e.g., layerwise, diagonal, and kronecker-factored) preconditioners for both online regret minimization and offline convex optimization. Our analysis not only provides matching rate to several important structured preconditioned algorithms including diagonal AdaGrad, full-matrix AdaGrad, and AdaGrad-Norm, but also gives an improved convergence rate for a one-sided variant of Shampoo over that of original Shampoo. Interestingly, more structured preconditioners (e.g., diagonal Adagrad, AdaGrad-Norm which use less space and compute) are often presented as computationally efficient approximations to full-matrix Adagrad, aiming for improved optimization performance through better approximations. Our unified analysis challenges this prevailing view and reveals, perhaps surprisingly, that more structured preconditioners, despite using less space and computation per step, can outperform their less structured counterparts. To demonstrate this, we show that one-sided Shampoo, which is relatively much cheaper than full-matrix AdaGrad could outperform it both theoretically and experimentally.
|
Shampoo, adaptive optimization, layerwise
| null | 13,872 |
2503.10537
|
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] | null |
443 |
Synthesizing Privacy-Preserving Text Data via Finetuning *without* Finetuning Billion-Scale LLMs
|
https://openreview.net/forum?id=FCm4laCLiH
|
[
"Bowen Tan",
"Zheng Xu",
"Eric P. Xing",
"Zhiting Hu",
"Shanshan Wu"
] |
Poster
|
Synthetic data offers a promising path to train models while preserving data privacy. Differentially private (DP) finetuning of large language models (LLMs) as data generator is effective, but is impractical when computation resources are limited. Meanwhile, prompt-based methods such as private evolution depend heavily on the manual prompts, and ineffectively use private information in their iterative data selection process. To overcome these limitations, we propose CTCL (Data Synthesis with **C**on**T**rollability and **CL**ustering), a novel framework for generating privacy-preserving synthetic data without extensive prompt engineering or billion-scale LLM finetuning. CTCL pretrains a lightweight 140M conditional generator and a clustering-based topic model on large-scale public data. To further adapt to the private domain, the generator is DP finetuned on private data for fine-grained textual information, while the topic model extracts a DP histogram representing distributional information. The DP generator then samples according to the DP histogram to synthesize a desired number of data examples. Evaluation across five diverse domains demonstrates the effectiveness of our framework, particularly in the strong privacy regime. Systematic ablation validates the design of each framework component and highlights the scalability of our approach.
|
synthetic data, language model, differential privacy
|
We propose a novel framework for generating privacy-preserving synthetic data without extensive prompt engineering or billion-scale LLM finetuning.
| 13,871 |
2503.12347
|
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] | null |
444 |
CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition
|
https://openreview.net/forum?id=HnXElKZdEh
|
[
"Zebin Wang",
"Menghan Lin",
"Bolin Shen",
"Ken Anderson",
"Molei Liu",
"Tianxi Cai",
"Yushun Dong"
] |
Poster
|
Graph Neural Networks (GNNs) have demonstrated remarkable utility across diverse applications, and their growing complexity has made Machine Learning as a Service (MLaaS) a viable platform for scalable deployment. However, this accessibility also exposes GNN to serious security threats, most notably model extraction attacks (MEAs), in which adversaries strategically query a deployed model to construct a high-fidelity replica. In this work, we evaluate the vulnerability of GNNs to MEAs and explore their potential for cost-effective model acquisition in non-adversarial research settings. Importantly, adaptive node querying strategies can also serve a critical role in research, particularly when labeling data is expensive or time-consuming. By selectively sampling informative nodes, researchers can train high-performing GNNs with minimal supervision, which is particularly valuable in domains such as biomedicine, where annotations often require expert input. To address this, we propose a node querying strategy tailored to a highly practical yet underexplored scenario, where bulk queries are prohibited, and only a limited set of initial nodes is available. Our approach iteratively refines the node selection mechanism over multiple learning cycles, leveraging historical feedback to improve extraction efficiency. Extensive experiments on benchmark graph datasets demonstrate our superiority over comparable baselines on accuracy, fidelity, and F1 score under strict query-size constraints. These results highlight both the susceptibility of deployed GNNs to extraction attacks and the promise of ethical, efficient GNN acquisition methods to support low-resource research environments. Our implementation is publicly available at [https://github.com/LabRAI/CEGA](https://github.com/LabRAI/CEGA).
|
GNN Model Extraction, Cost Efficiency, Budget Limitation
|
This paper introduces CEGA, a novel Graph Neural Networks model extraction framework to efficiently select query nodes under strict budget constraints, exhibiting superior performance over alternatives.
| 13,870 |
2506.17709
|
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] |
https://github.com/LabRAI/CEGA
|
445 |
Observation Interference in Partially Observable Assistance Games
|
https://openreview.net/forum?id=rjZ2SWjwwB
|
[
"Scott Emmons",
"Caspar Oesterheld",
"Vincent Conitzer",
"Stuart Russell"
] |
Poster
|
We study partially observable assistance games (POAGs), a model of the human-AI value alignment problem which allows the human and the AI assistant to have partial observations. Motivated by concerns of AI deception, we study a qualitatively new phenomenon made possible by partial observability: would an AI assistant ever have an incentive to interfere with the human's observations? First, we prove that sometimes an optimal assistant must take observation-interfering _actions_, even when the human is playing optimally, and even when there are otherwise-equivalent actions available that do not interfere with observations. Though this result seems to contradict the classic theorem from single-agent decision making that the value of perfect information is nonnegative, we resolve this seeming contradiction by developing a notion of interference defined on entire _policies_. This can be viewed as an extension of the classic result that the value of perfect information is nonnegative into the cooperative multiagent setting. Second, we prove that if the human is simply making decisions based on their immediate outcomes, the assistant might need to interfere with observations as a way to query the human's preferences. We show that this incentive for interference goes away if the human is playing optimally, or if we introduce a communication channel for the human to communicate their preferences to the assistant. Third, we show that if the human acts according to the Boltzmann model of irrationality, this can create an incentive for the assistant to interfere with observations. Finally, we use an experimental model to analyze tradeoffs faced by the AI assistant in practice when considering whether or not to take observation-interfering actions.
|
assistance games, AI alignment, observation interference, partial observability, partially observable assistance games
|
We study when an AI assistant would have an incentive to interfere with a human's observations.
| 13,868 |
2412.17797
|
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] | null |
446 |
Feasible Action Search for Bandit Linear Programs via Thompson Sampling
|
https://openreview.net/forum?id=GrF14Q0DNW
|
[
"Aditya Gangrade",
"Aldo Pacchiano",
"Clayton Scott",
"Venkatesh Saligrama"
] |
Poster
|
We study the 'feasible action search' (FAS) problem for linear bandits, wherein a learner attempts to discover a feasible point for a set of linear constraints $\Phi_* a \ge 0,$ without knowledge of the matrix $\Phi_* \in \mathbb{R}^{m \times d}$. A FAS learner selects a sequence of actions $a_t,$ and uses observations of the form $\Phi_* a_t + \mathrm{noise}$ to either find a point with nearly optimal 'safety margin', or detect that the constraints are infeasible, where the safety margin of an action measures its (signed) distance from the constraint boundary. While of interest in its own right, the FAS problem also directly addresses a key deficiency in the extant theory of 'safe linear bandits' (SLBs), by discovering a safe initialisation for low-regret SLB methods.
We propose and analyse a novel efficient FAS-learner. Our method, FAST, is based on Thompson Sampling. It applies a _coupled_ random perturbation to an estimate of $\Phi_*,$ and plays a maximin point of a game induced by this perturbed matrix. We prove that FAST stops in $\tilde{O}(d^3/\varepsilon^2 M_*^2)$ steps, and incurs $\tilde{O}(d^3/|M_*|)$ safety costs, to either correctly detect infeasibility, or output a point that is at least $(1-\varepsilon) M_*$-safe, where $M_*$ is the _optimal safety margin_ of $\Phi_*$. Further, instantiating prior SLB methods with the output of FAS yields the first SLB methods that incur $\tilde{O}(\sqrt{d^3 T/M_*^2})$ regret and $O(1)$ risk without a priori knowledge of a safe action. The main technical novelty lies in the extension of Thompson Sampling to this multiobjective setting, for which we both propose a coupled noise design, and provide an analysis that avoids convexity considerations.
|
Safe Bandits, Linear Bandits, Thompson Sampling
|
An efficient method based on Thompson Sampling to find feasible actions for LPs with bandit feedback
| 13,853 | null |
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] | null |
447 |
SafetyAnalyst: Interpretable, Transparent, and Steerable Safety Moderation for AI Behavior
|
https://openreview.net/forum?id=WUGrleBcYP
|
[
"Jing-Jing Li",
"Valentina Pyatkin",
"Max Kleiman-Weiner",
"Liwei Jiang",
"Nouha Dziri",
"Anne Collins",
"Jana Schaich Borg",
"Maarten Sap",
"Yejin Choi",
"Sydney Levine"
] |
Poster
|
The ideal AI safety moderation system would be both structurally interpretable (so its decisions can be reliably explained) and steerable (to align to safety standards and reflect a community's values), which current systems fall short on. To address this gap, we present SafetyAnalyst, a novel AI safety moderation framework. Given an AI behavior, SafetyAnalyst uses chain-of-thought reasoning to analyze its potential consequences by creating a structured "harm-benefit tree," which enumerates harmful and beneficial *actions* and *effects* the AI behavior may lead to, along with *likelihood*, *severity*, and *immediacy* labels that describe potential impacts on *stakeholders*. SafetyAnalyst then aggregates all effects into a harmfulness score using 28 fully interpretable weight parameters, which can be aligned to particular safety preferences. We applied this framework to develop an open-source LLM prompt safety classification system, distilled from 18.5 million harm-benefit features generated by frontier LLMs on 19k prompts. On comprehensive benchmarks, we show that SafetyAnalyst (average F1=0.81) outperforms existing moderation systems (average F1$<$0.72) on prompt safety classification, while offering the additional advantages of interpretability, transparency, and steerability.
|
AI safety, large language model, interpretability, content moderation
|
We propose a novel framework for interpretable safety moderation of AI behavior.
| 13,822 |
2410.16665
|
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] | null |
448 |
Introducing 3D Representation for Dense Volume-to-Volume Translation via Score Fusion
|
https://openreview.net/forum?id=UHVk08XFkX
|
[
"Xiyue Zhu",
"Dou Hoon Kwark",
"Ruike Zhu",
"Kaiwen Hong",
"Yiqi Tao",
"Shirui Luo",
"Yudu Li",
"Zhi-Pei Liang",
"Volodymyr Kindratenko"
] |
Poster
|
In volume-to-volume translations in medical images, existing models often struggle to capture the inherent volumetric distribution using 3D voxel-space representations, due to high computational dataset demands. We present Score-Fusion, a novel volumetric translation model that effectively learns 3D representations by ensembling perpendicularly trained 2D diffusion models in score function space. By carefully initializing our model to start with an average of 2D models as in existing models, we reduce 3D training to a fine-tuning process, mitigating computational and data demands. Furthermore, we explicitly design the 3D model's hierarchical layers to learn ensembles of 2D features, further enhancing efficiency and performance. Moreover, Score-Fusion naturally extends to multi-modality settings by fusing diffusion models conditioned on different inputs for flexible, accurate integration. We demonstrate that 3D representation is essential for better performance in downstream recognition tasks, such as tumor segmentation, where most segmentation models are based on 3D representation. Extensive experiments demonstrate that Score-Fusion achieves superior accuracy and volumetric fidelity in 3D medical image super-resolution and modality translation. Additionally, we extend Score-Fusion to video super-resolution by integrating 2D diffusion models on time-space slices with a spatial-temporal video diffusion backbone, highlighting its potential for general-purpose volume translation and providing broader insight into learning-based approaches for score function fusion.
|
Diffusion models, 3D medical image generation, video generation
|
We find it hard to train 3D volume translation models in inverse problems that require high accuracy, such as super-resolution. We effectively introduced 3D representation by ensmbling 2D models' results with a 3D model.
| 13,813 | null |
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] | null |
449 |
LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits
|
https://openreview.net/forum?id=Fm0nDMKBwC
|
[
"Zikai Zhou",
"Qizheng Zhang",
"Hermann Kumbong",
"Kunle Olukotun"
] |
Poster
|
Fine-tuning large language models (LLMs) is increasingly costly as models scale to hundreds of billions of parameters, and even parameter-efficient fine-tuning (PEFT) methods like LoRA remain resource-intensive.
We introduce LowRA, the first framework to enable LoRA fine-tuning below 2 bits per parameter with minimal performance loss.
LowRA optimizes fine-grained quantization—mapping, threshold selection, and precision assignment—while leveraging efficient CUDA kernels for scalable deployment.
Extensive evaluations across 4 LLMs and 4 datasets show that LowRA achieves a superior performance–precision trade-off above 2 bits and remains accurate down to 1.15 bits, reducing memory usage by up to 50\%.
Our results highlight the potential of ultra-low-bit LoRA fine-tuning for resource-constrained environments.
|
Quantization, LoRA, PEFT
|
We introduce LowRA to enable LoRA fine-tuning below 2 bits per parameter with minimal performance loss, cutting memory use by up to 50%.
| 13,809 |
2502.08141
|
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] | null |
450 |
Breaking the Barrier of Hard Samples: A Data-Centric Approach to Synthetic Data for Medical Tasks
|
https://openreview.net/forum?id=SJkpCMeIxu
|
[
"MAYNARA DONATO DE SOUZA",
"Cleber Zanchettin"
] |
Poster
|
Data scarcity and quality issues remain significant barriers to developing robust predictive models in medical research. Traditional reliance on real-world data often leads to biased models with poor generalizability across diverse patient populations. Synthetic data generation has emerged as a promising solution, yet challenges related to these sample's representativeness and effective utilization persist. This paper introduces Profile2Gen, a novel data-centric framework designed to guide the generation and refinement of synthetic data, focusing on addressing hard-to-learn samples in regression tasks. We conducted approximately 18,000 experiments to validate its effectiveness across six medical datasets, utilizing seven state-of-the-art generative models. Results demonstrate that refined synthetic samples can reduce predictive errors and enhance model reliability. Additionally, we generalize the DataIQ framework to support regression tasks, enabling its application in broader contexts. Statistical analyses confirm that our approach achieves equal or superior performance compared to models trained exclusively on real data.
|
Medical tasks, synthetic data, data-centric
| null | 13,807 | null |
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] |
https://github.com/szanara/profile2gen
|
451 |
Imitation Learning from a Single Temporally Misaligned Video
|
https://openreview.net/forum?id=YV05KZt7v2
|
[
"William Huey",
"Huaxiaoyue Wang",
"Anne Wu",
"Yoav Artzi",
"Sanjiban Choudhury"
] |
Poster
|
We examine the problem of learning sequential tasks from a single visual demonstration.
A key challenge arises when demonstrations are temporally misaligned due to variations in timing, differences in embodiment, or inconsistencies in execution. Existing approaches treat imitation as a distribution-matching problem, aligning individual frames between the agent and the demonstration. However, we show that such frame-level matching fails to enforce temporal ordering or ensure consistent progress.
Our key insight is that matching should instead be defined at the level of sequences.
We propose that perfect matching occurs when one sequence successfully covers all the subgoals in the same order as the other sequence.
We present ORCA (ORdered Coverage Alignment), a dense per-timestep reward function that measures the probability of the agent covering demonstration frames in the correct order.
On temporally misaligned demonstrations, we show that agents trained with the ORCA reward achieve $4.5$x improvement ($0.11 \rightarrow 0.50$ average normalized returns) for Meta-world tasks and $6.6$x improvement ($6.55 \rightarrow 43.3$ average returns) for Humanoid-v4 tasks compared to the best frame-level matching algorithms.
We also provide empirical analysis showing that ORCA is robust to varying levels of temporal misalignment. The project website is at https://portal-cornell.github.io/orca/
|
Learning from Videos, Inverse Reinforcement Learning, Reward Formulation
|
Learning sequential tasks from a temporally misaligned video requires a reward function that measures subgoal ordering and coverage.
| 13,799 |
2502.05397
|
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] |
https://github.com/portal-cornell/orca
|
452 |
The Logical Implication Steering Method for Conditional Interventions on Transformer Generation
|
https://openreview.net/forum?id=E7c9Jf1KjV
|
[
"Damjan Kalajdzievski"
] |
Poster
|
The field of mechanistic interpretability in pre-trained transformer models has demonstrated substantial evidence supporting the ''linear representation hypothesis'', which is the idea that high level concepts are encoded as vectors in the space of activations of a model. Studies also show that model generation behavior can be steered toward a given concept by adding the concept's vector to the corresponding activations. We show how to leverage these properties to build a form of logical implication into models, enabling transparent and interpretable adjustments that induce a chosen generation behavior in response to the presence of any given concept. Our method, Logical Implication Model Steering (LIMS), unlocks new hand-engineered reasoning capabilities by integrating neuro-symbolic logic into pre-trained transformer models.
|
Machine Learning, ICML, Mechanistic Interpretability, Interpretability, Safety, LLM, Transformer, Neuro-Symbolic, Language Modelling, Transformer, Steering
|
We show how to linear concepts in the representation space of a transformer to build a form of logical implication into models.
| 13,790 |
2502.03618
|
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] | null |
453 |
Variational Phylogenetic Inference with Products over Bipartitions
|
https://openreview.net/forum?id=s1WJSRaJuy
|
[
"Evan Sidrow",
"Alexandre Bouchard-Cote",
"Lloyd T Elliott"
] |
Poster
|
Bayesian phylogenetics is vital for understanding evolutionary dynamics, and requires accurate and efficient approximation of posterior distributions over trees. In this work, we develop a variational Bayesian approach for ultrametric phylogenetic trees. We present a novel variational family based on coalescent times of a single-linkage clustering and derive a closed-form density for the resulting distribution over trees. Unlike existing methods for ultrametric trees, our method performs inference over all of tree space, it does not require any Markov chain Monte Carlo subroutines, and our variational family is differentiable. Through experiments on benchmark genomic datasets and an application to the viral RNA of SARS-CoV-2, we demonstrate that our method achieves competitive accuracy while requiring significantly fewer gradient evaluations than existing state-of-the-art techniques.
|
Phylogenetic Inference, Variational Bayes, COVID-19 Genetics, Linkage Clustering, Reinforce Estimators
|
We develop a variational inference approach for ultrametric phylogenetic trees that is differentiable, doesn't restrict tree space, and doesn't rely on MCMC subroutines.
| 13,788 |
2502.15110
|
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] |
https://github.com/EvanSidrow/VIPR
|
454 |
Shifting Time: Time-series Forecasting with Khatri-Rao Neural Operators
|
https://openreview.net/forum?id=emkdmORaj4
|
[
"Srinath Dama",
"Kevin Course",
"Prasanth B. Nair"
] |
Poster
|
We present an operator-theoretic framework for temporal and spatio-temporal forecasting based on learning a *continuous time-shift operator*. Our operator learning paradigm offers a continuous relaxation of the discrete lag factor used in traditional autoregressive models, enabling the history of a system up to a given time to be mapped to its future values. We parametrize the time-shift operator using Khatri-Rao neural operators (KRNOs), a novel architecture based on non-stationary integral transforms with nearly linear computational scaling. Our framework naturally handles irregularly sampled observations and enables forecasting at super-resolution in both space and time. Extensive numerical studies across diverse temporal and spatio-temporal benchmarks demonstrate that our approach achieves state-of-the-art or competitive performance with leading methods.
|
spatio-temporal modeling, time-series modeling, time-shift operator, Khatri-Rao neural operator, neural operator, operator learning, time-series forecasting
|
We present a new operator-theoretic paradigm for temporal and spatio-temporal forecasting problems by learning a continuous time-shift operator.
| 13,773 | null |
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] |
https://github.com/srinathdama/ShiftingTime
|
455 |
Constrained Online Convex Optimization with Polyak Feasibility Steps
|
https://openreview.net/forum?id=EAAjvpE7sp
|
[
"Spencer Hutchinson",
"Mahnoosh Alizadeh"
] |
Poster
|
In this work, we study online convex optimization with a fixed constraint function $g : \mathbb{R}^d \rightarrow \mathbb{R}$. Prior work on this problem has shown $O(\sqrt{T})$ regret and cumulative constraint satisfaction $\sum_{t=1}^{T} g(x_t) \leq 0$, while only accessing the constraint value and subgradient at the played actions $g(x_t), \partial g(x_t)$. Using the same constraint information, we show a stronger guarantee of anytime constraint satisfaction $g(x_t) \leq 0 \ \forall t \in [T]$, and matching $O(\sqrt{T})$ regret guarantees. These contributions are thanks to our approach of using Polyak feasibility steps to ensure constraint satisfaction, without sacrificing regret. Specifically, after each step of online gradient descent, our algorithm applies a subgradient descent step on the constraint function where the step-size is chosen according to the celebrated Polyak step-size. We further validate this approach with numerical experiments.
|
online convex optimization, online learning, constraints
| null | 13,771 |
2502.13112
|
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] |
https://github.com/shutch1/OCO-Polyak-Feasibility-Steps
|
456 |
Graph-Based Algorithms for Diverse Similarity Search
|
https://openreview.net/forum?id=dmN2fQ3woH
|
[
"Piyush Anand",
"Piotr Indyk",
"Ravishankar Krishnaswamy",
"Sepideh Mahabadi",
"Vikas C. Raykar",
"Kirankumar Shiragur",
"Haike Xu"
] |
Poster
|
Nearest neighbor search is a fundamental data structure problem with many applications. Although the main objective of the data structure is to quickly report data points that are closest to a given query, it has long been noted that without additional constraints the reported answers can be redundant and/or duplicative. This issue is typically addressed in two stages: in the first stage, the algorithm retrieves a (large) number $r$ of points closest to the query, while in the second stage, the $r$ points are post-processed and a small subset is selected to maximize the desired diversity objective. Although popular, this method suffers from a fundamental efficiency bottleneck, as the set of points retrieved in the first stage often needs to be much larger than the final output. In this paper we present provably efficient algorithms for approximate nearest neighbor search with diversity constraints that bypass this two stage process. Our algorithms are based on popular graph-based methods, which allows us to ``piggy-back'' on the existing efficient implementations. These are the first graph-based algorithms for nearest neighbor search with diversity constraints. For data sets with low intrinsic dimension, our data structures report a diverse set of $k$ points approximately closest to the query, in time that only depends on $k$ and $\log \Delta$, where $\Delta$ is the ratio of the diameter to the closest pair distance in the data set. This bound is qualitatively similar to the best known bounds for standard (non-diverse) graph-based algorithms. Our experiments show that the search time of our algorithms is substantially lower than that using the standard two-stage approach.
|
nearest neighbor search; diversity; algorithms
|
This paper presents provably efficient algorithms for incorporating diversity into the results of graph-based similarity search algorithms.
| 13,770 |
2502.13336
|
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] |
https://github.com/microsoft/DiskANN/tree/diversity
|
457 |
Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation
|
https://openreview.net/forum?id=zZXOXhxO6I
|
[
"Tianyi Zhang",
"Junda Su",
"Aditya Desai",
"Oscar Wu",
"Zhaozhuo Xu",
"Anshumali Shrivastava"
] |
Poster
|
Adapting pre-trained large language models (LLMs) is crucial but challenging due to their enormous size. Parameter-efficient fine-tuning (PEFT) techniques typically employ additive adapters applied to frozen model weights. To further reduce memory usage, model weights are often compressed through quantization. However, existing PEFT methods often yield suboptimal model quality because they rely on restrictive assumptions, such as low-rank constraints on adapters to limit the number of trainable parameters. We find that sketching, a popular data compression technique, can serve as an efficient LLM adaptation strategy while avoiding the low-rank assumption. We introduce SketchTune, a compressive adaptation strategy that compresses LLM weights into compact fine-tunable sketches, integrating compression and adaptation into a unified framework. This integration eliminates the need for complex two-path computation in existing PEFT techniques, enabling faster and more memory-efficient training and inference. SketchTune is supported by mathematical insights into matrix classes that are better approximated using sketching rather than low-rank methods. Our extensive evaluations with Llama and Mistral models demonstrate that SketchTune outperforms leading PEFT methods across diverse tasks while using substantially smaller base models and comparable trainable parameters. As a highlight, SketchTune outperforms LoRA, DoRA, and S2FT on commonsense and math benchmarks using 2.6-3.5$\times$ smaller base models and exceeds LoftQ in accuracy by 14.48\% on GSM8K with 7.3$\times$ fewer trainable parameters.
|
large language models, fine-tuning, sketching
| null | 13,768 |
2410.06364
|
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] |
https://github.com/LeanModels/SketchTune
|
458 |
RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior
|
https://openreview.net/forum?id=NbjrGgxLPi
|
[
"Ching-Hua Lee",
"Chouchang Yang",
"Jaejin Cho",
"Yashas Malur Saidutta",
"Rakshith Sharma Srinivasa",
"Yilin Shen",
"Hongxia Jin"
] |
Poster
|
Denoising diffusion probabilistic models (DDPMs) can be utilized to recover a clean signal from its degraded observation(s) by conditioning the model on the degraded signal. The degraded signals are themselves contaminated versions of the clean signals; due to this correlation, they may encompass certain useful information about the target clean data distribution. However, existing adoption of the standard Gaussian as the prior distribution in turn discards such information when shaping the prior, resulting in sub-optimal performance. In this paper, we propose to improve conditional DDPMs for signal restoration by leveraging a more informative prior that is jointly learned with the diffusion model. The proposed framework, called RestoreGrad, seamlessly integrates DDPMs into the variational autoencoder (VAE) framework, taking advantage of the correlation between the degraded and clean signals to encode a better diffusion prior. On speech and image restoration tasks, we show that RestoreGrad demonstrates faster convergence (5-10 times fewer training steps) to achieve better quality of restored signals over existing DDPM baselines and improved robustness to using fewer sampling steps in inference time (2-2.5 times fewer), advocating the advantages of leveraging jointly learned prior for efficiency improvements in the diffusion process.
|
Denoising diffusion probabilistic model, prior distribution, posterior, speech enhancement, image restoration
|
This paper proposes an integration of conditional denoising diffusion probabilistic models (DDPMs) into the variational autoencoder (VAE) framework to jointly learn a more informative diffusion prior in signal restoration applications.
| 13,761 |
2502.13574
|
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] | null |
459 |
Adversarial Reasoning at Jailbreaking Time
|
https://openreview.net/forum?id=aWd7mL5U9Q
|
[
"Mahdi Sabbaghi",
"Paul Kassianik",
"George J. Pappas",
"Amin Karbasi",
"Hamed Hassani"
] |
Poster
|
As large language models (LLMs) are becoming more capable and widespread, the study of their failure cases is becoming increasingly important. Recent advances in standardizing, measuring, and scaling test-time compute suggest new methodologies for optimizing models to achieve high performance on hard tasks. In this paper, we apply these advances to the task of model jailbreaking: eliciting harmful responses from aligned LLMs. We develop an adversarial reasoning approach to automatic jailbreaking that leverages a loss signal to guide the test-time compute, achieving SOTA attack success rates against many aligned LLMs, even those that aim to trade inference-time compute for adversarial robustness. Our approach introduces a new paradigm in understanding LLM vulnerabilities, laying the foundation for the development of more robust and trustworthy AI systems.
|
LLMs, Jailbreaking, Reasoning, Test-time compute.
|
We employ a reasoning-driven framework to formulate and solve the jailbreaking problem as an optimization task.
| 13,747 |
2502.01633
|
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] |
https://github.com/Helloworld10011/Adversarial-Reasoning
|
460 |
Hierarchical Equivariant Policy via Frame Transfer
|
https://openreview.net/forum?id=nAv5ketrHq
|
[
"Haibo Zhao",
"Dian Wang",
"Yizhe Zhu",
"Xupeng Zhu",
"Owen Lewis Howell",
"Linfeng Zhao",
"Yaoyao Qian",
"Robin Walters",
"Robert Platt"
] |
Poster
|
Recent advances in hierarchical policy learning highlight the advantages of decomposing systems into high-level and low-level agents, enabling efficient long-horizon reasoning and precise fine-grained control. However, the interface between these hierarchy levels remains underexplored, and existing hierarchical methods often ignore domain symmetry, resulting in the need for extensive demonstrations to achieve robust performance. To address these issues, we propose Hierarchical Equivariant Policy (HEP), a novel hierarchical policy framework. We propose a frame transfer interface for hierarchical policy learning, which uses the high-level agent's output as a coordinate frame for the low-level agent, providing a strong inductive bias while retaining flexibility. Additionally, we integrate domain symmetries into both levels and theoretically demonstrate the system's overall equivariance. HEP achieves state-of-the-art performance in complex robotic manipulation tasks, demonstrating significant improvements in both simulation and real-world settings.
|
robot learning, imitation learning, robotic manipulation, equivariance
|
We propose a equivariant hierarchical policy learning framework for visuomotor policy learning
| 13,739 |
2502.05728
|
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] | null |
461 |
Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion
|
https://openreview.net/forum?id=GGPM0z3dhU
|
[
"Kulin Shah",
"Alkis Kalavasis",
"Adam Klivans",
"Giannis Daras"
] |
Poster
|
There is strong empirical evidence that the stateof-the-art diffusion modeling paradigm leads to models that memorize the training set, especially when the training set is small. Prior methods to mitigate the memorization problem often lead to decrease in image quality. Is it possible to obtain strong and creative generative models, i.e., models that achieve high generation quality and low memorization? Despite the current pessimistic landscape of results, we make significant progress in pushing the trade-off between fidelity and memorization. We first provide theoretical evidence that memorization in diffusion models is only necessary for denoising problems at low noise scales (usually used in generating high-frequency details). Using this theoretical insight, we propose a simple, principled method to train the diffusion models using noisy data at large noise scales. We show that our method significantly reduces memorization without decreasing the image quality, for both text-conditional and unconditional models and for a variety of data availability settings.
|
diffusion, memorization, corrupted data, limited samples, generative models, ambient diffusion
|
We study to what extent is possible to train powerful generative models without memorizing the training set.
| 13,735 |
2502.21278
|
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] |
https://github.com/kulinshah98/memorization_noisy_data
|
462 |
Dialogue Without Limits: Constant-Sized KV Caches for Extended Response in LLMs
|
https://openreview.net/forum?id=SuYO70ZxZX
|
[
"Ravi Ghadia",
"Avinash Kumar",
"Gaurav Jain",
"Prashant J. Nair",
"Poulami Das"
] |
Poster
|
Autoregressive Transformers rely on Key-Value (KV) caching to accelerate inference. However, the linear growth of the KV cache with context length leads to excessive memory consumption and bandwidth constraints. Existing methods drop distant tokens or compress states in a lossy manner, sacrificing accuracy by discarding vital context or introducing bias.
We propose ${MorphKV}$, an inference-time technique that maintains a constant-sized KV cache while preserving accuracy. MorphKV balances long-range dependencies and local coherence during text generation. It eliminates early-token bias while retaining high-fidelity context by adaptively ranking tokens through correlation-aware selection. Unlike heuristic retention or lossy compression, MorphKV iteratively refines the KV cache via lightweight updates guided by attention patterns of recent tokens. This approach captures inter-token correlation with greater accuracy, which is crucial for tasks like content creation and code generation. Our studies on long-response tasks show 52.9\% memory savings and 18.2\% higher accuracy on average compared to state-of-the-art prior works, enabling efficient deployment.
|
Large Language Model, Key-Value Cache Compression
|
An effective KV compression technique for long-response benchmarks
| 13,734 |
2503.00979
|
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] |
https://github.com/ghadiaravi13/MorphKV
|
463 |
Test-Time Graph Neural Dataset Search With Generative Projection
|
https://openreview.net/forum?id=824TCt6CkE
|
[
"Xin Zheng",
"Wei Huang",
"Chuan Zhou",
"Ming Li",
"Shirui Pan"
] |
Poster
|
In this work, we address the test-time adaptation challenge in graph neural networks (GNNs), focusing on overcoming the limitations in flexibility and generalization inherent in existing data-centric approaches. To this end, we propose a novel research problem, test-time graph neural dataset search, which seeks to learn a parameterized test-time graph distribution to enhance the inference performance of unseen test graphs on well-trained GNNs. Specifically, we propose a generative Projection based test-time Graph Neural Dataset Search method, named PGNDS, which maps the unseen test graph distribution back to the known training distribution through a generation process guided by well-trained GNNs. The proposed PGNDS framework consists of three key modules: (1) dual conditional diffusion for GNN-guided generative projection through test-back-to-training distribution mapping; (2) dynamic search from the generative sampling space to select the most expressive test graphs; (3) ensemble inference to aggregate information from original and adapted test graphs. Extensive experiments on real-world graphs demonstrate the superior ability of our proposed PGNDS for improved test-time GNN inference.
|
Test-Time Adaption (TTA); Graph Neural Networks (GNNs); Distribution Shift; Generative Models
|
Generative test-time graph adaptation method with distribution learning
| 13,731 | null |
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] | null |
464 |
What Has a Foundation Model Found? Inductive Bias Reveals World Models
|
https://openreview.net/forum?id=i9npQatSev
|
[
"Keyon Vafa",
"Peter G. Chang",
"Ashesh Rambachan",
"Sendhil Mullainathan"
] |
Poster
|
Foundation models are premised on the idea that sequence prediction can uncover deeper domain understanding, much like how Kepler's predictions of planetary motion later led to the discovery of Newtonian mechanics. However, evaluating whether these models truly capture deeper structure remains a challenge. We develop a technique for evaluating foundation models that examines how they adapt to synthetic datasets generated from some postulated world model. Our technique measures whether the foundation model's inductive bias aligns with the world model, and so we refer to it as an inductive bias probe. Across multiple domains, we find that foundation models can excel at their training tasks yet fail to develop inductive biases towards the underlying world model when adapted to new tasks. We particularly find that foundation models trained on orbital trajectories consistently fail to apply Newtonian mechanics when adapted to new physics tasks. Further analysis reveals that these models behave as if they develop task-specific heuristics that fail to generalize.
|
world models, foundation models, inductive bias, large language models
|
We develop inductive bias probes to test whether foundation models have inductive biases toward specific world models.
| 13,728 | null |
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] | null |
465 |
AlphaPO: Reward Shape Matters for LLM Alignment
|
https://openreview.net/forum?id=LmdZ0pSWtG
|
[
"Aman Gupta",
"Shao Tang",
"Qingquan Song",
"Sirou Zhu",
"Jiwoo Hong",
"Ankan Saha",
"Viral Gupta",
"Noah Lee",
"Eunki Kim",
"Siyu Zhu",
"Parag Agrawal",
"Natesh S. Pillai",
"Sathiya Keerthi"
] |
Poster
|
Reinforcement Learning with Human Feedback (RLHF) and its variants have made huge strides toward the effective alignment of large language models (LLMs) to follow instructions and reflect human values. More recently, Direct Alignment Algorithms (DAAs) have emerged in which the reward modeling stage of RLHF is skipped by characterizing the reward directly as a function of the policy being learned. Some popular examples of DAAs include Direct Preference Optimization (DPO) and Simple Preference Optimization (SimPO). These methods often suffer from likelihood displacement, a phenomenon by which the probabilities of preferred responses are often reduced undesirably. In this paper, we argue that, for DAAs the reward (function) shape matters. We introduce \textbf{AlphaPO}, a new DAA method that leverages an $\alpha$-parameter to help change the shape of the reward function beyond the standard log reward. AlphaPO helps maintain fine-grained control over likelihood displacement and over-optimization. Compared to SimPO, one of the best performing DAAs, AlphaPO leads to about 7\% to 10\% relative improvement in alignment performance for the instruct versions of Mistral-7B and Llama3-8B while achieving 15\% to 50\% relative improvement over DPO on the same models. The analysis and results presented highlight the importance of the reward shape and how one can systematically change it to affect training dynamics, as well as improve alignment performance.
|
llm, large language models, deep learning, alignment, preference tuning, post training, reward shaping
|
Use a parameter alpha to control the shape of the reward function for alignment training, in order to improve performance
| 13,723 |
2501.03884
|
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] | null |
466 |
Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization
|
https://openreview.net/forum?id=sQ6lqdjGBX
|
[
"Emiliano Penaloza",
"Tianyue H. Zhang",
"Laurent Charlin",
"Mateo Espinosa Zarlenga"
] |
Poster
|
Concept Bottleneck Models (CBMs) propose to
enhance the trustworthiness of AI systems by
constraining their decisions on a set of human
understandable concepts. However, CBMs typically rely on datasets with assumedly accurate
concept labels—an assumption often violated in
practice which we show can significantly degrade
performance. To address this, we introduce the
Concept Preference Optimization (CPO) objective, a new loss function based on Direct Preference Optimization, which effectively mitigates
the negative impact of concept mislabeling on
CBM performance. We provide an analysis on
some key properties of the CPO objective showing it directly optimizes for the concept’s posterior
distribution, and contrast it against Binary Cross
Entropy (BCE) where we show CPO is inherently
less sensitive to concept noise. We empirically
confirm our analysis finding that CPO consistently
outperforms BCE in three real-world datasets with
and without added label noise
|
Concept Bottleneck Models, Interpretable AI, XAI
| null | 13,713 |
2504.18026
|
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] |
https://github.com/Emilianopp/ConceptPreferenceOptimization
|
467 |
Near-optimal Sketchy Natural Gradients for Physics-Informed Neural Networks
|
https://openreview.net/forum?id=bKsZomnmqn
|
[
"Maricela Best Mckay",
"Avleen Kaur",
"Chen Greif",
"Brian Wetton"
] |
Poster
|
Natural gradient methods for PINNs have achieved state-of-the-art performance with errors several orders of magnitude smaller than those achieved by standard optimizers such as ADAM or L-BFGS. However, computing natural gradients for PINNs is prohibitively computationally costly and memory-intensive for all but small neural network architectures. We develop a randomized algorithm for natural gradient descent for PINNs that uses sketching to approximate the natural gradient descent direction. We prove that the change of coordinate Gram matrix used in a natural gradient descent update has rapidly-decaying eigenvalues for a one-layer, one-dimensional neural network and empirically demonstrate that this structure holds for four different example problems. Under this structure, our sketching algorithm is guaranteed to provide a near-optimal low-rank approximation of the Gramian. Our algorithm dramatically speeds up computation time and reduces memory overhead. Additionally, in our experiments, the sketched natural gradient outperforms the original natural gradient in terms of accuracy, often achieving an error that is an order of magnitude smaller. Training time for a network with around 5,000 parameters is reduced from several hours to under two minutes. Training can be practically scaled to large network sizes; we optimize a PINN for a network with over a million parameters within a few minutes, a task for which the full Gram matrix does not fit in memory.
|
Sci-ML, Physics Informed Neural Networks, Natural Gradients, Sketching
| null | 13,711 | null |
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] | null |
468 |
KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors
|
https://openreview.net/forum?id=WBN0Mz3VAC
|
[
"Benson Chen",
"Tomasz Danel",
"Gabriel H. S. Dreiman",
"Patrick J. McEnaney",
"Nikhil Jain",
"Kirill Novikov",
"Spurti Umesh Akki",
"Joshua L. Turnbull",
"Virja Atul Pandya",
"Boris P. Belotserkovskii",
"Jared Bryce Weaver",
"Ankita Biswas",
"Dat Nguyen",
"Kent Gorday",
"Mohammad Sultan",
"Nathaniel Stanley",
"Daniel M Whalen",
"Divya Kanichar",
"Christoph Klein",
"Emily Fox",
"et al. (1 additional authors not shown)"
] |
Poster
|
DNA-Encoded Libraries (DELs) represent a transformative technology in drug discovery, facilitating the high-throughput exploration of vast chemical spaces. Despite their potential, the scarcity of publicly available DEL datasets presents a bottleneck for the advancement of machine learning methodologies in this domain. To address this gap, we introduce KinDEL, one of the largest publicly accessible DEL datasets and the first one that includes binding poses from molecular docking experiments. Focused on two kinases, Mitogen-Activated Protein Kinase 14 (MAPK14) and Discoidin Domain Receptor Tyrosine Kinase 1 (DDR1), KinDEL includes 81 million compounds, offering a rich resource for computational exploration. Additionally, we provide comprehensive biophysical assay validation data, encompassing both on-DNA and off-DNA measurements, which we use to evaluate a suite of machine learning techniques, including novel structure-based probabilistic models. We hope that our benchmark, encompassing both 2D and 3D structures, will help advance the development of machine learning models for data-driven hit identification using DELs.
|
DEL, small molecule, dataset, benchmark, drug discovery
|
Dataset and benchmark paper for a 81 million small molecule DNA-encoded library to find hits for drug discovery
| 13,707 |
2410.08938
|
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] |
https://github.com/insitro/kindel
|
469 |
UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction
|
https://openreview.net/forum?id=5Rtj4mYH1C
|
[
"Shravan Nayak",
"Xiangru Jian",
"Kevin Qinghong Lin",
"Juan A. Rodriguez",
"Montek Kalsi",
"Nicolas Chapados",
"M. Tamer Özsu",
"Aishwarya Agrawal",
"David Vazquez",
"Christopher Pal",
"Perouz Taslakian",
"Spandana Gella",
"Sai Rajeswar"
] |
Poster
|
Autonomous agents that navigate Graphical User Interfaces (GUIs) to automate tasks like document editing and file management can greatly enhance computer workflows. While existing research focuses on online settings, desktop environments, critical for many professional and everyday tasks, remain underexplored due to data collection challenges and licensing issues. We introduce UI-Vision, the first comprehensive, license-permissive benchmark for offline, fine-grained evaluation of computer use agents in real-world desktop environments. Unlike online benchmarks, UI-Vision provides: (i) dense, high-quality annotations of human demonstrations, including bounding boxes, UI labels, and action trajectories (clicks, drags, and keyboard inputs) across 83 software applications, and (ii) three fine-to-coarse grained tasks—Element Grounding, Layout Grounding, and Action Prediction—with well-defined metrics to rigorously evaluate agents' performance in desktop environments. Our evaluation reveals critical limitations in state-of-the-art models like UI-TARS-72B, including issues with understanding professional software, spatial reasoning, and complex actions like drag-and-drop. These findings highlight the challenges in developing fully autonomous computer-use agents. With UI-Vision, we aim to advance the development of more capable agents for real-world desktop tasks.
|
Large language models, Multimodal models, LLMs, VLMs, Autonomous agents, GUI
|
We introduce UI-Vision, a comprehensive desktop-centric and license-permissive GUI understanding benchmark
| 13,703 | null |
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] |
https://github.com/uivision/UI-Vision
|
470 |
Online Laplacian-Based Representation Learning in Reinforcement Learning
|
https://openreview.net/forum?id=NXtoNstR96
|
[
"Maheed H. Ahmed",
"Jayanth Bhargav",
"Mahsa Ghasemi"
] |
Poster
|
Representation learning plays a crucial role in reinforcement learning, especially in complex environments with high-dimensional and unstructured states. Effective representations can enhance the efficiency of learning algorithms by improving sample efficiency and generalization across tasks. This paper considers the Laplacian-based framework for representation learning, where the eigenvectors of the Laplacian matrix of the underlying transition graph are leveraged to encode meaningful features from raw sensory observations of the states. Despite the promising algorithmic advances in this framework, it remains an open question whether the Laplacian-based representations can be learned online and with theoretical guarantees along with policy learning. We address this by formulating an online optimization approach using the Asymmetric Graph Drawing Objective (AGDO) and analyzing its convergence via online projected gradient descent under mild assumptions. Our extensive simulation studies empirically validate the convergence guarantees to the true Laplacian representation. Furthermore, we provide insights into the compatibility of different reinforcement learning algorithms with online representation learning.
|
Reinforcement Learning, Representation learning, Online Learning, Graph Laplacian
|
We propose an online method for learning the Laplacian representation in reinforcement learning, and show theoretically and empirically it converges.
| 13,699 | null |
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] |
https://github.com/MaheedHatem/online_laplacian_representation
|
471 |
Modulated Diffusion: Accelerating Generative Modeling with Modulated Quantization
|
https://openreview.net/forum?id=y8hMadAgrz
|
[
"Weizhi Gao",
"Zhichao Hou",
"Junqi Yin",
"Feiyi Wang",
"Linyu Peng",
"Xiaorui Liu"
] |
Poster
|
Diffusion models have emerged as powerful generative models, but their high computation cost in iterative sampling remains a significant bottleneck. In this work, we present an in-depth and insightful study of state-of-the-art acceleration techniques for diffusion models, including caching and quantization, revealing their limitations in computation error and generation quality. To break these limits, this work introduces Modulated Diffusion (MoDiff), an innovative, rigorous, and principled framework that accelerates generative modeling through modulated quantization and error compensation. MoDiff not only inherents the advantages of existing caching and quantization methods but also serves as a general framework to accelerate all diffusion models. The advantages of MoDiff are supported by solid theoretical insight and analysis. In addition, extensive experiments on CIFAR-10 and LSUN demonstrate that MoDiff significant reduces activation quantization from 8 bits to 3 bits without performance degradation in post-training quantization (PTQ). Our code implementation is available at https://github.com/WeizhiGao/MoDiff.
|
Diffusion Models; Efficiency; Quantization; Caching Method
| null | 13,693 |
2506.22463
|
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] |
https://github.com/WeizhiGao/MoDiff
|
472 |
Memorization Sinks: Isolating Memorization during LLM Training
|
https://openreview.net/forum?id=sRJrMPu5Uu
|
[
"Gaurav Rohit Ghosal",
"Pratyush Maini",
"Aditi Raghunathan"
] |
Poster
|
Large language models are susceptible to memorizing repeated sequences, posing privacy and copyright concerns. A popular mitigation strategy is to remove memorized information from specific neurons post-hoc. However, such approaches have shown limited success so far. In a controlled setting, we show that the memorization of *natural* sequences (those that resemble linguistically plausible text) become *mechanistically entangled* with general language abilities, thereby becoming challenging to remove post-hoc. In this work, we put forward a new paradigm of MemSinks that promotes isolation of memorization by design. We leverage a sequence identifier to activate a unique set of memorization neurons for each sequence across repetitions. By analyzing the dynamics of learning and forgetting, we argue that MemSinks facilitates clean isolation of memorized content, making it easier to remove without compromising general language capabilities. We implement MemSinks at the billion-parameter and billion-token scale, and observe both effective isolation and strong generalization. To our knowledge, this is the first proof-of-concept on real data demonstrating that simultaneous generalization and isolation is achievable. We open-source our code at http://github.com/grghosal/MemSinks.
|
Memorization, Localization, Unlearning
|
We propose a method to disentangle sequence memorization and general language model capabilities during pretraining.
| 13,672 |
2507.09937
|
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] |
https://github.com/grghosal/MemSinks
|
473 |
Skip the Equations: Learning Behavior of Personalized Dynamical Systems Directly From Data
|
https://openreview.net/forum?id=2gpjvMEAMm
|
[
"Krzysztof Kacprzyk",
"Julianna Piskorz",
"Mihaela van der Schaar"
] |
Poster
|
While black-box approaches are commonly used for data-driven modeling of dynamical systems, they often obscure a system's underlying behavior and properties, limiting adoption in areas such as medicine and pharmacology. A two-step process of discovering ordinary differential equations (ODEs) and their subsequent mathematical analysis can yield insights into the system's dynamics. However, this analysis may be infeasible for complex equations, and refining the ODE to meet certain behavioral requirements can be challenging. Direct semantic modeling has recently been proposed to address these issues by predicting the system's behavior, such as the trajectory's shape, directly from data, bypassing post-hoc mathematical analysis. In this work, we extend the original instantiation, limited to one-dimensional trajectories and inputs, to accommodate multi-dimensional trajectories with additional personalization, allowing evolution to depend on auxiliary static features (e.g., patient covariates). In a series of experiments, we show how our approach enables practitioners to integrate prior knowledge, understand the dynamics, ensure desired behaviors, and revise the model when necessary.
|
dynamical systems, differential equations, ODE discovery, interpretability, transparency, verification
|
An interpretable framework for trajectory prediction from static features: a decision tree identifies the curve’s global shape, while generalized additive models assign the key properties such as start, peak, or inflection.
| 13,671 | null |
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] |
https://github.com/krzysztof-kacprzyk/EPISODE
|
474 |
Return Capping: Sample Efficient CVaR Policy Gradient Optimisation
|
https://openreview.net/forum?id=ebf2IYBrZO
|
[
"Harry Mead",
"Clarissa Costen",
"Bruno Lacerda",
"Nick Hawes"
] |
Poster
|
When optimising for conditional value at risk (CVaR) using policy gradients (PG), current methods rely on discarding a large proportion of trajectories, resulting in poor sample efficiency. We propose a reformulation of the CVaR optimisation problem by capping the total return of trajectories used in training, rather than simply discarding them, and show that this is equivalent to the original problem if the cap is set appropriately. We show, with empirical results in an number of environments, that this reformulation of the problem results in consistently improved performance compared to baselines. We have made all our code available here: \url{https://github.com/HarryMJMead/cvar-return-capping}.
|
Reinforcement Learning, Machine Learning, CVaR, Risk-Averse
|
We present a sample-efficient method for policy gradient CVaR optimisation by capping trajectory returns, rather than discarding trajectories.
| 13,664 |
2504.20887
|
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-0.004218772519379854,
-0.009894959628582,
-0.04194359481334686,
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] |
https://github.com/HarryMJMead/cvar-return-capping
|
475 |
LensLLM: Unveiling Fine-Tuning Dynamics for LLM Selection
|
https://openreview.net/forum?id=om0CcjvEQh
|
[
"Xinyue Zeng",
"Haohui Wang",
"Junhong Lin",
"Jun Wu",
"Tyler Cody",
"Dawei Zhou"
] |
Poster
|
The proliferation of open-sourced Large Language Models (LLMs) and diverse downstream tasks necessitates efficient model selection, given the impracticality of fine-tuning all candidates due to computational constraints. Despite the recent advances in LLM selection, a fundamental research question largely remains nascent: *how can we model the dynamic behaviors of LLMs during fine-tuning, thereby enhancing our understanding of their generalization performance across diverse downstream tasks?* In this work, we propose a novel theoretical framework that provides a proper lens to assess the generalization capabilities of LLMs, thereby enabling accurate and efficient LLM selection for downstream applications. In particular, we first derive a *PAC-Bayesian Generalization Bound* that unveils fine-tuning dynamics of LLMs and then introduce *LensLLM*, a Neural Tangent Kernel (NTK)-based Rectified Scaling Model that enables accurate performance predictions across diverse tasks while maintaining computational efficiency. Extensive empirical results on 3 large-scale benchmarks demonstrate that our model achieves up to 91.1% accuracy and reduces up to 88.5% computational cost in LLM selection, outperforming 5 state-of-the-art methods. We open-source our proposed *LensLLM* model and corresponding results at [LensLLM.io](https://github.com/Susan571/LENSLLM.git).
|
LLM Selection, PAC-Bayesian Theory, Generalization Bound, Scaling Laws
|
We derive a PAC-Bayesian generalization bound for LLM fine-tuning dynamics and propose LENSLLM, a framework that enables accurate, efficient performance prediction across diverse tasks.
| 13,652 |
2505.03793
|
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] |
https://github.com/Susan571/LENSLLM
|
476 |
A New Concentration Inequality for Sampling Without Replacement and Its Application for Transductive Learning
|
https://openreview.net/forum?id=NRVdvg7VMn
|
[
"Yingzhen Yang"
] |
Poster
|
We introduce a new tool, Transductive Local Complexity (TLC), to analyze the generalization performance of transductive learning methods and motivate new transductive learning algorithms. Our work extends the idea of the popular Local Rademacher Complexity (LRC) to the transductive setting with considerable and novel changes compared to the analysis of typical LRC methods in the inductive setting. While LRC has been widely used as a powerful tool in the analysis of inductive models with sharp generalization bounds for classification and minimax rates for nonparametric regression, it remains an open problem whether a localized version of Rademacher complexity based tool can be designed and applied to transductive learning and gain sharp bound for transductive learning which is consistent with the inductive excess risk bound by (LRC). We give a confirmative answer to this open problem by TLC. Similar to the development of LRC, we build TLC by first establishing a novel and sharp concentration inequality for supremum of empirical processes for the gap between test and training loss in the setting of sampling uniformly without replacement. Then a peeling strategy and a new surrogate variance operator are used to derive the following excess risk bound in the transductive setting, which is consistent with that of the classical LRC based excess risk bound in the inductive setting.
As an application of TLC, we use the new TLC tool to analyze the Transductive Kernel Learning (TKL) model, and derive sharper excess risk bound than that by the current state-of-the-art. As a result of independent interest, the concentration inequality for the test-train process is used to derive a sharp concentration inequality for the general supremum of empirical process involving random variables in the setting of sampling uniformly without replacement, with comparison to current concentration inequalities.
|
Concentration Inequality, Sampling Without Replacement, Transductive Local Rademacher Complexity, Transductive Learning
|
We present a new concentration inequality for the supremum of the empirical process associated with sampling without replacement, and apply our new concentration inequality to prove sharper generalization bound for transductive kernel learning.
| 13,647 | null |
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] | null |
477 |
Improving Diversity in Language Models: When Temperature Fails, Change the Loss
|
https://openreview.net/forum?id=RsyMfsqzeG
|
[
"Alexandre Verine",
"Florian Le Bronnec",
"Kunhao Zheng",
"Alexandre Allauzen",
"Yann Chevaleyre",
"benjamin negrevergne"
] |
Poster
|
Increasing diversity in language models is a challenging yet essential objective. A common approach is to raise the decoding temperature. In this work, we investigate this approach through a simplistic yet common case to provide insights into why decreasing temperature can improve quality (Precision), while increasing it often fails to boost coverage (Recall). Our analysis reveals that for a model to be effectively tunable through temperature adjustments, it must be trained toward coverage. To address this, we propose rethinking loss functions in language models by leveraging the Precision-Recall framework. Our results demonstrate that this approach achieves a substantially better trade-off between Precision and Recall than merely combining negative log-likelihood training with temperature scaling. These findings offer a pathway toward more versatile and robust language modeling techniques.
|
Language Models, Diversity, Precision, Recall, Temperature
|
We propose losses to improve diversity in language models to compensate for temperature failing.
| 13,645 | null |
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] | null |
478 |
PENCIL: Long Thoughts with Short Memory
|
https://openreview.net/forum?id=6wglsDXIei
|
[
"Chenxiao Yang",
"Nathan Srebro",
"David McAllester",
"Zhiyuan Li"
] |
Poster
|
While state-of-the-art LLMs have demonstrated great promise of using long Chains-of-Thought (CoT) to boost reasoning, scaling it up to more challenging problems is fundamentally limited by suboptimal memory usage — intermediate computations accumulate indefinitely in context even no longer needed for future thoughts. We introduce PENCIL, which incorporates a novel reduction mechanism into the autoregressive generation process that recursively clean up intermediate thoughts based on patterns learned from training. By alternately generating and erasing, PENCIL can think deeper to solve harder problems using shorter context and less computes. Empirically, for example, we demonstrate PENCIL with a small 25M-parameter transformer and 2048 context length solves Einstein's puzzle — a task that challenges much larger models like GPT-4. Theoretically, we prove PENCIL can perform universal efficient computation by simulating any Turing machines with optimal time and space complexity, and thus can solve arbitrary computable tasks that are otherwise intractable for vanilla CoT.
|
Large language models, chain-of-thought
| null | 13,637 |
2503.14337
|
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] |
https://github.com/chr26195/PENCIL
|
479 |
Universal Neural Optimal Transport
|
https://openreview.net/forum?id=t10fde8tQ7
|
[
"Jonathan Geuter",
"Gregor Kornhardt",
"Ingimar Tomasson",
"Vaios Laschos"
] |
Poster
|
Optimal Transport (OT) problems are a cornerstone of many applications, but solving them is computationally expensive. To address this problem, we propose UNOT (Universal Neural Optimal Transport), a novel framework capable of accurately predicting (entropic) OT distances and plans between discrete measures of variable resolution for a given cost function. UNOT builds on Fourier Neural Operators, a universal class of neural networks that map between function spaces and that are discretization-invariant, which enables our network to process measures of varying sizes. The network is trained adversarially using a second, generating network and a self-supervised bootstrapping loss. We theoretically justify the use of FNOs, prove that our generator is universal, and that minimizing the bootstrapping loss provably minimizes the ground truth loss. Through extensive experiments, we show that our network not only accurately predicts optimal transport distances and plans across a wide range of datasets, but also captures the geometry of the Wasserstein space correctly. Furthermore, we show that our network can be used as a state-of-the-art initialization for the Sinkhorn algorithm, significantly outperforming existing approaches.
|
Optimal Transport, Neural Operators, Meta Learning, Adversarial Training
|
We train a neural network to accurately predict optimal transport distances across datasets and dimensions with neural operators.
| 13,626 |
2212.00133
|
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] |
https://github.com/GregorKornhardt/UNOT
|
480 |
Policy-Regret Minimization in Markov Games with Function Approximation
|
https://openreview.net/forum?id=eZ5QyZV7zi
|
[
"Thanh Nguyen-Tang",
"Raman Arora"
] |
Poster
|
We study policy-regret minimization problem in dynamically evolving environments, modeled as Markov games between a learner and a strategic, adaptive opponent. We propose a general algorithmic framework that achieves the optimal $\mathcal{O}(\sqrt{T})$ policy regret for a wide class of large-scale problems characterized by an Eluder-type condition--extending beyond the tabular settings of previous work. Importantly, our framework uncovers a simpler yet powerful algorithmic approach for handling reactive adversaries, demonstrating that leveraging opponent learning in such settings is key to attaining the optimal $\mathcal{O}(\sqrt{T})$ policy regret.
|
policy regret, Markov games, strategic opponents, function approximation, online learning, reinforcement learning, adversarial learning, Eluder dimension, multi-agent learning
|
A general algorithm that provably minimizes policy regret in complex games with strategic opponents.
| 13,580 | null |
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] | null |
481 |
MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations
|
https://openreview.net/forum?id=OZy70UggXr
|
[
"Kaixuan Huang",
"Jiacheng Guo",
"Zihao Li",
"Xiang Ji",
"Jiawei Ge",
"Wenzhe Li",
"Yingqing Guo",
"Tianle Cai",
"Hui Yuan",
"Runzhe Wang",
"Yue Wu",
"Ming Yin",
"Shange Tang",
"Yangsibo Huang",
"Chi Jin",
"Xinyun Chen",
"Chiyuan Zhang",
"Mengdi Wang"
] |
Poster
|
Large language models have demonstrated impressive performance on challenging mathematical reasoning tasks, which has triggered the discussion of whether the performance is achieved by true reasoning capability or memorization. To investigate this question, prior work has constructed mathematical benchmarks when questions undergo simple perturbations -- modifications that still preserve the underlying reasoning patterns of the solutions. However, no work has explored hard perturbations, which fundamentally change the nature of the problem so that the original solution steps do not apply. To bridge the gap, we construct MATH-P-Simple and MATH-P-Hard via simple perturbation and hard perturbation, respectively. Each consists of 279 perturbed math problems derived from level-5 (hardest) problems in the MATH dataset (Hendrycks et al., 2021). We observe significant performance drops on MATH-P-Hard across various models, including o1-mini (-16.49%) and gemini-2.0-flash-thinking (-12.9%). We also raise concerns about a novel form of memorization where models blindly apply learned problem-solving skills without assessing their applicability to modified contexts. This issue is amplified when using original problems for in-context learning. We call for research efforts to address this challenge, which is critical for developing more robust and reliable reasoning models. The project is available at https://math-perturb.github.io/.
|
mathematical reasoning, benchmark, robustness
|
We construct MATH-P-Simple and MATH-P-Hard to benchmark LLM's math reasoning against simple and hard perturbations, and examine memorization issues.
| 13,579 |
2502.06453
|
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] | null |
482 |
DeepCrossAttention: Supercharging Transformer Residual Connections
|
https://openreview.net/forum?id=j3JBfFnGYh
|
[
"Mike Heddes",
"Adel Javanmard",
"Kyriakos Axiotis",
"Gang Fu",
"Mohammadhossein Bateni",
"Vahab Mirrokni"
] |
Poster
|
Transformer networks have achieved remarkable success across diverse domains, leveraging a variety of architectural innovations, including residual connections. However, traditional residual connections, which simply sum the outputs of previous layers, can dilute crucial information. This work introduces DeepCrossAttention (DCA), an approach that enhances residual learning in transformers. DCA employs learnable, input-dependent weights to dynamically combine layer outputs, enabling the model to selectively focus on the most relevant information in any of the previous layers. Furthermore, DCA incorporates depth-wise cross-attention, allowing for richer interactions between layers at different depths. Our language modeling experiments show that DCA achieves improved perplexity for a given training time. Moreover, DCA obtains the same model quality up to 3x faster while adding a negligible number of parameters (e.g., 0.2\%). Theoretical analysis confirms that DCA provides an improved trade-off between accuracy and model size when the ratio of collective layer ranks to the ambient dimension falls below a critical threshold.
|
residual network, cross attention, resnet, transformer
|
We speed up transformer training by introducing learnable, input-dependent residual connections combined with depth-wise cross attention.
| 13,577 |
2502.06785
|
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] | null |
483 |
Steerable Transformers for Volumetric Data
|
https://openreview.net/forum?id=Ax550Vokon
|
[
"Soumyabrata Kundu",
"Risi Kondor"
] |
Poster
|
We introduce Steerable Transformers, an extension of the Vision Transformer mechanism that maintains equivariance to the special Euclidean group $\mathrm{SE}(d)$. We propose an equivariant attention mechanism that operates on features extracted by steerable convolutions. Operating in Fourier space, our network utilizes Fourier space non-linearities. Our experiments in both two and three dimensions show that adding steerable transformer layers to steerable convolutional networks enhances performance.
|
Equivariance, transformers, vision transformers, steerable
|
SE(d) equivariant transformfers for volumetric data
| 13,573 |
2405.15932
|
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] |
https://github.com/SoumyabrataKundu/Steerable-Transformer
|
484 |
Value-Based Deep RL Scales Predictably
|
https://openreview.net/forum?id=FLPFPYJeVU
|
[
"Oleh Rybkin",
"Michal Nauman",
"Preston Fu",
"Charlie Victor Snell",
"Pieter Abbeel",
"Sergey Levine",
"Aviral Kumar"
] |
Poster
|
Scaling data and compute is critical in modern machine learning. However, scaling also demands _predictability_: we want methods to not only perform well with more compute or data, but also have their performance be predictable from low compute or low data runs, without ever running the large-scale experiment. In this paper, we show predictability of value-based off-policy deep RL. First, we show that data and compute requirements to reach a given performance level lie on a _Pareto frontier_, controlled by the updates-to-data (UTD) ratio. By estimating this frontier, we can extrapolate data requirements into a higher compute regime, and compute requirements into a higher data regime. Second, we determine the optimal allocation of total _budget_ across data and compute to obtain given performance and use it to determine hyperparameters that maximize performance for a given budget. Third, this scaling behavior is enabled by first estimating predictable relationships between different _hyperparameters_, which is used to counteract effects of overfitting and plasticity loss unique to RL. We validate our approach using three algorithms: SAC, BRO, and PQL on DeepMind Control, OpenAI gym, and IsaacGym, when extrapolating to higher levels of data, compute, budget, or performance.
|
scaling laws, online reinforcement learning, q-learning
|
We establish that value-based online RL can be scaled predictably to larger data, larger compute, or generally larger budget
| 13,569 |
2502.04327
|
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] | null |
485 |
Hardware and Software Platform Inference
|
https://openreview.net/forum?id=kdmjVF1iDO
|
[
"Cheng Zhang",
"Hanna Foerster",
"Robert D. Mullins",
"Yiren Zhao",
"Ilia Shumailov"
] |
Poster
|
It is now a common business practice to buy access to large language model (LLM) inference rather than self-host, because of significant upfront hardware infrastructure and energy costs. However, as a buyer, there is no mechanism to verify the authenticity of the advertised service including the serving hardware platform, e.g. that it is actually being served using an NVIDIA H100. Furthermore, there are reports suggesting that model providers may deliver models that differ slightly from the advertised ones, often to make them run on less expensive hardware. That way, a client pays premium for a capable model access on more expensive hardware, yet ends up being served by a (potentially less capable) cheaper model on cheaper hardware. In this paper we introduce ***hardware and software platform inference (HSPI)*** -- a method for identifying the underlying GPU architecture and software stack of a (black-box) machine learning model solely based on its input-output behavior. Our method leverages the inherent differences of various GPU architectures and compilers to distinguish between different GPU types and software stacks. By analyzing the numerical patterns in the model's outputs, we propose a classification framework capable of accurately identifying the GPU used for model inference as well as the underlying software configuration. Our findings demonstrate the feasibility of inferring GPU type from black-box models. We evaluate HSPI against models served on different real hardware and find that in a white-box setting we can distinguish between different GPUs with between 83.9% and 100% accuracy. Even in a black-box setting we are able to achieve results that are up to three times higher than random guess accuracy.
|
ML security, ML governance
|
We introduce a new problem and solutions to identifying the software and hardware platform of an ML model solely based on its input-output behaviour. Abstract:
| 13,561 |
2411.05197
|
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] |
https://github.com/ChengZhang-98/HSPI
|
486 |
Putnam-AXIOM: A Functional & Static Benchmark for Measuring Higher Level Mathematical Reasoning in LLMs
|
https://openreview.net/forum?id=kqj2Cn3Sxr
|
[
"Aryan Gulati",
"Brando Miranda",
"Eric Chen",
"Emily Xia",
"Kai Fronsdal",
"Bruno de Moraes Dumont",
"Sanmi Koyejo"
] |
Poster
|
Current mathematical reasoning benchmarks for large language models (LLMs) are approaching saturation, with some achieving $>$ 90% accuracy, and are increasingly compromised by training-set contamination.
We introduce Putnam-AXIOM, a benchmark of 522 university-level competition problems drawn from the prestigious William Lowell Putnam Mathematical Competition, and Putnam-AXIOM Variation, an unseen companion set of 100 functional variants generated by programmatically perturbing variables, and constants.
The variation protocol produces an unlimited stream of equally difficult, unseen instances -- yielding a contamination-resilient test bed.
On the Original set, OpenAI's o1-preview – the strongest evaluated model – scores 41.9%, but its accuracy drops by 19.6 % (46.8% relative decrease) on the paired Variations.
The remaining eighteen models show the same downward trend, ten of them with non-overlapping 95% confidence intervals.
These gaps suggest memorization and highlight the necessity of dynamic benchmarks.
We complement ("boxed") accuracy with Teacher-Forced Accuracy (TFA), a lightweight metric that directly scores reasoning traces and automates natural language proof evaluations.
Putnam-AXIOM therefore provides a rigorous, contamination-resilient evaluation framework for assessing advanced mathematical reasoning of LLMs.
Data and evaluation code are publicly available at
https://github.com/brando90/putnam-axiom.
|
Benchmarks, Large Language Models, Mathematical Reasoning, Mathematics, Reasoning, Machine Learning
|
Putnam-AXIOM is a challenging mathematical reasoning benchmark for LLMs, revealing significant reasoning performance gaps and the impact of data contamination.
| 13,558 | null |
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] |
https://github.com/brando90/putnam-axiom
|
487 |
Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow
|
https://openreview.net/forum?id=6uPcJtMgWN
|
[
"Zhonglin Cao",
"Mario Geiger",
"Allan Dos Santos Costa",
"Danny Reidenbach",
"Karsten Kreis",
"Tomas Geffner",
"Franco Pellegrini",
"Guoqing Zhou",
"Emine Kucukbenli"
] |
Poster
|
Fast and accurate generation of molecular conformers is desired for downstream computational chemistry and drug discovery tasks. Currently, training and sampling state-of-the-art diffusion or flow-based models for conformer generation require significant computational resources. In this work, we build upon flow-matching and propose two mechanisms for accelerating training and inference of generative models for 3D molecular conformer generation. For fast training, we introduce the SO(3)-*Averaged Flow* training objective, which leads to faster convergence to better generation quality compared to conditional optimal transport flow or Kabsch-aligned flow. We demonstrate that models trained using SO(3)-*Averaged Flow* can reach state-of-the-art conformer generation quality. For fast inference, we show that the reflow and distillation methods of flow-based models enable few-steps or even one-step molecular conformer generation with high quality. The training techniques proposed in this work show a path towards highly efficient molecular conformer generation with flow-based models.
|
Flow-matching, few-shot generation, equivariance, small molecules
|
We present a novel flow-matching objective and combine it with reflow to accelerate training and inference of molecular conformer generation
| 13,550 | null |
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] | null |
488 |
Do Vision-Language Models Really Understand Visual Language?
|
https://openreview.net/forum?id=ZPQU4uGMBA
|
[
"Yifan Hou",
"Buse Giledereli",
"Yilei Tu",
"Mrinmaya Sachan"
] |
Poster
|
Visual language is a system of communication that conveys information through symbols, shapes, and spatial arrangements. Diagrams are a typical example of a visual language depicting complex concepts and their relationships in the form of an image. The symbolic nature of diagrams presents significant challenges for building models capable of understanding them. Yet, recent studies seem to suggest that Large Vision-Language Models (LVLMs) can even tackle complex reasoning tasks involving diagrams. In this paper, we investigate this phenomenon by developing a comprehensive test suite to evaluate the diagram comprehension capability of LVLMs. Our test suite uses a variety of questions focused on concept entities and their relationships over a set of synthetic as well as real diagrams across several domains to evaluate the recognition and reasoning abilities of models. Our evaluation of six LVLMs shows that while these models can accurately identify and reason about entities, their ability to understand relationships is notably limited. Further testing reveals that the decent performance on diagram understanding largely stems from leveraging their background knowledge as shortcuts to identify and reason about the relational information. Thus, we conclude that LVLMs have a limited capability for genuine diagram understanding, and their impressive performance in diagram reasoning is an illusion emanating from other confounding factors, such as the background knowledge in the models.
|
vision-language model, visual language, evaluation, interpretation, diagram
| null | 13,533 |
2410.00193
|
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] |
https://github.com/buseg/diagram-understanding
|
489 |
What can large language models do for sustainable food?
|
https://openreview.net/forum?id=f6SFHNfuMu
|
[
"Anna Thomas",
"Adam Yee",
"Andrew Mayne",
"Maya B. Mathur",
"Dan Jurafsky",
"Kristina Gligorić"
] |
Poster
|
Food systems are responsible for a third of human-caused greenhouse gas emissions. We investigate what Large Language Models (LLMs) can contribute to reducing the environmental impacts of food production. We define a typology of design and prediction tasks based on the sustainable food literature and collaboration with domain experts, and evaluate six LLMs on four tasks in our typology. For example, for a sustainable protein design task, food science experts estimated that collaboration with an LLM can reduce time spent by 45% on average, compared to 22% for collaboration with another expert human food scientist. However, for a sustainable menu design task, LLMs produce suboptimal solutions when instructed to consider both human satisfaction and climate impacts. We propose a general framework for integrating LLMs with combinatorial optimization to improve reasoning capabilities. Our approach decreases emissions of food choices by 79% in a hypothetical restaurant while maintaining participants' satisfaction with their set of choices. Our results demonstrate LLMs' potential, supported by optimization techniques, to accelerate sustainable food development and adoption.
|
large language models, sustainability, climate, food, health, optimization
|
Motivated by food systems' major role in climate change, we define a typology of sustainable food development tasks, demonstrate expert-level LLM performance, and propose a novel framework integrating LLMs with combinatorial optimization.
| 13,531 |
2503.04734
|
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] |
https://github.com/thomasat/llms-sustainable-food
|
490 |
No Free Lunch from Random Feature Ensembles: Scaling Laws and Near-Optimality Conditions
|
https://openreview.net/forum?id=z9GgK3CK39
|
[
"Benjamin Samuel Ruben",
"William Lingxiao Tong",
"Hamza Tahir Chaudhry",
"Cengiz Pehlevan"
] |
Poster
|
Given a fixed budget for total model size, one must choose between training a single large model or combining the predictions of multiple smaller models.
We investigate this trade-off for ensembles of random-feature ridge regression models in both the overparameterized and underparameterized regimes.
Using deterministic equivalent risk estimates, we prove that when a fixed number of parameters is distributed among $K$ independently trained models, the ridge-optimized test risk increases with $K$.
Consequently, a single large model achieves optimal performance. We then ask when ensembles can achieve *near*-optimal performance.
In the overparameterized regime, we show that, to leading order, the test error depends on ensemble size and model size only through the total feature count, so that overparameterized ensembles consistently achieve near-optimal performance.
To understand underparameterized ensembles, we derive scaling laws for the test risk as a function of total parameter count when the ensemble size and parameters per ensemble member are jointly scaled according to a ``growth exponent'' $\ell$.
While the optimal error scaling is always achieved by increasing model size with a fixed ensemble size, our analysis identifies conditions on the kernel and task eigenstructure under which near-optimal scaling laws can be obtained by joint scaling of ensemble size and model size.
|
Ensemble Learning, Regression, Scaling Laws
|
We show that ensembles of random feature models never outperform a single model of the same total size, and identify conditions where near-optimal performance by ensembles is possible in both the overparameterized and underparameterized regimes.
| 13,527 | null |
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] | null |
491 |
GSM-∞: How Do your LLMs Behave over Infinitely Increasing Reasoning Complexity and Context Length?
|
https://openreview.net/forum?id=n52yyvEwPa
|
[
"Yang Zhou",
"Hongyi Liu",
"Zhuoming Chen",
"Yuandong Tian",
"Beidi Chen"
] |
Poster
|
Recently, long-context large language models (LLMs) have shown strong performance in information retrieval and long-document QA. However, to tackle the most challenging intellectual problems, LLMs must reason effectively in long and complex contexts (e.g., frontier mathematical research). Studying how LLMs handle increasing reasoning complexity and context length is essential, yet existing benchmarks lack a solid basis for quantitative evaluation. Inspired by the abstraction of GSM-8K problems as computational graphs—and the ability to introduce noise by adding unnecessary nodes and edges—we develop a grade-school math problem generator capable of producing arithmetic problems with infinite difficulty and context length under fine-grained control. Using our newly synthesized GSM-$\infty$ benchmark, we comprehensively evaluate existing LLMs. We find a consistent sigmoid decline in reasoning performance as complexity increases, along with a systematic inference scaling trend: exponentially increasing inference computation yields only linear performance gains. These findings underscore the fundamental limitations of current long-context LLMs and the key challenges in scaling reasoning capabilities. Our GSM-$\infty$ benchmark provides a scalable and controllable testbed for systematically studying and advancing LLM reasoning in long and complex contexts.
|
Long Context, Reasoning, Understanding, Benchmarks
|
We study LLMs reasoning ability decay with respect to increasingly harder problems and with respect to increasing context length through synthesized dataset generator that generates fine-grainedly controlled GSM8K-like problems.
| 13,525 | null |
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] |
https://github.com/Infini-AI-Lab/gsm_infinite
|
492 |
Improving Model Alignment Through Collective Intelligence of Open-Source Models
|
https://openreview.net/forum?id=K4N9UvsuNB
|
[
"Junlin Wang",
"Roy Xie",
"Shang Zhu",
"Jue WANG",
"Ben Athiwaratkun",
"Bhuwan Dhingra",
"Shuaiwen Leon Song",
"Ce Zhang",
"James Zou"
] |
Poster
|
Building helpful and harmless large language models (LLMs) requires effective model alignment approach based on human instructions and feedback, which necessitates high-quality human-labeled data. Constructing such datasets is often expensive and hard to scale, and may face potential limitations on diversity and generalization. To address these challenges, we introduce Mixture of Agents Alignment (MoAA), that leverages the collective strengths of various language models to provide high-quality data for model alignment. By employing MoAA, we enhance both supervised fine-tuning and preference optimization, leading to improved performance compared to using a single model alone to generate alignment data (e.g. using GPT-4o alone). Evaluation results show that our approach can improve win rate of LLaMA-3.1-8B-Instruct from 19.5 to 48.3 on Arena-Hard and from 22.33 to 57.23 on AlpacaEval2, highlighting a promising direction for model alignment through this new scalable and diverse synthetic data recipe. Furthermore, we demonstrate that MoAA enables a self-improvement pipeline, where models fine-tuned on MoA-generated data surpass their own initial capabilities, providing evidence that our approach can push the frontier of open-source LLMs without reliance on stronger external supervision. Data and code will be released.
|
Alignment, Open-Source Model, Mixture of Agents
|
We show how to leverage mixture-of-agents to generate synthetic data and feedback to effectively align models.
| 13,514 |
2505.03059
|
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] | null |
493 |
Robust Reward Alignment via Hypothesis Space Batch Cutting
|
https://openreview.net/forum?id=pL87Z7YTJS
|
[
"Zhixian Xie",
"Haode Zhang",
"Yizhe Feng",
"Wanxin Jin"
] |
Poster
|
Reward design in reinforcement learning and optimal control is challenging. Preference-based alignment addresses this by enabling agents to learn rewards from ranked trajectory pairs provided by humans. However, existing methods often struggle from poor robustness to unknown false human preferences. In this work, we propose a robust and efficient reward alignment method based on a novel and geometrically interpretable perspective: hypothesis space batched cutting. Our method iteratively refines the reward hypothesis space through “cuts” based on batches of human preferences. Within each batch, human preferences, queried based on disagreement, are grouped using a voting function to determine the appropriate cut, ensuring a bounded human query complexity. To handle unknown erroneous preferences, we introduce a conservative cutting method within each batch, preventing erroneous human preferences from making overly aggressive cuts to the hypothesis space. This guarantees provable robustness against false preferences, while eliminating the need to explicitly identify them. We evaluate our method in a model predictive control setting across diverse tasks. The results demonstrate that our framework achieves comparable or superior performance to state-of-the-art methods in error-free settings while significantly outperforming existing methods when handling a high percentage of erroneous human preferences.
|
Learning from Human Feedback, Inverse Reinforcement Learning, Preference Based Reinforcement Learning, Robust Learning
| null | 13,507 |
2502.02921
|
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] |
https://github.com/asu-iris/HSBC-Robust-Learning
|
494 |
Convergence of Consistency Model with Multistep Sampling under General Data Assumptions
|
https://openreview.net/forum?id=vsJsR3ieCx
|
[
"Yiding Chen",
"Yiyi Zhang",
"Owen Oertell",
"Wen Sun"
] |
Poster
|
Diffusion models accomplish remarkable success in data generation tasks across various domains. However, the iterative sampling process is computationally expensive. Consistency models are proposed to learn consistency functions to map from noise to data directly, which allows one-step fast data generation and multistep sampling to improve sample quality. In this paper, we study the convergence of consistency models when the self-consistency property holds approximately under the training distribution. Our analysis requires only mild data assumption and applies to a family of forward processes. When the target data distribution has bounded support or has tails that decay sufficiently fast, we show that the samples generated by the consistency model are close to the target distribution in Wasserstein distance; when the target distribution satisfies some smoothness assumption, we show that with an additional perturbation step for smoothing, the generated samples are close to the target distribution in total variation distance. We provide two case studies with commonly chosen forward processes to demonstrate the benefit of multistep sampling.
|
Consistency models, diffusion models, learning theory
| null | 13,478 |
2505.03194
|
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] | null |
495 |
Wasserstein Policy Optimization
|
https://openreview.net/forum?id=oAKe7MG9GM
|
[
"David Pfau",
"Ian Davies",
"Diana L Borsa",
"João Guilherme Madeira Araújo",
"Brendan Daniel Tracey",
"Hado van Hasselt"
] |
Poster
|
We introduce Wasserstein Policy Optimization (WPO), an actor-critic algorithm for reinforcement learning in continuous action spaces. WPO can be derived as an approximation to Wasserstein gradient flow over the space of all policies projected into a finite-dimensional parameter space (e.g., the weights of a neural network), leading to a simple and completely general closed-form update. The resulting algorithm combines many properties of deterministic and classic policy gradient methods. Like deterministic policy gradients, it exploits knowledge of the *gradient* of the action-value function with respect to the action. Like classic policy gradients, it can be applied to stochastic policies with arbitrary distributions over actions -- without using the reparameterization trick. We show results on the DeepMind Control Suite and a magnetic confinement fusion task which compare favorably with state-of-the-art continuous control methods.
|
Policy Optimization, Wasserstein metric, Optimal Transport, Gradient Flow, Deep Reinforcement Learning, Actor-Critic, Continuous Control
|
We derive a novel policy gradient algorithm from Wasserstein gradient flows and show that it is simple and effective at deep reinforcement learning for continuous control.
| 13,462 |
2505.00663
|
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] |
https://github.com/google-deepmind/acme/blob/master/examples/baselines/rl_continuous/run_wpo.py
|
496 |
Simplifying DINO via Coding Rate Regularization
|
https://openreview.net/forum?id=shTZSlk0HQ
|
[
"Ziyang Wu",
"Jingyuan Zhang",
"Druv Pai",
"XuDong Wang",
"Chandan Singh",
"Jianwei Yang",
"Jianfeng Gao",
"Yi Ma"
] |
Poster
|
DINO and DINOv2 are two model families being widely used to learn representations from unlabeled imagery data at large scales. Their learned representations often enable state-of-the-art performance for downstream tasks, such as image classification and segmentation. However, they employ many empirically motivated design choices and their training pipelines are highly complex and unstable --- many hyperparameters need to be carefully tuned to ensure that the representations do not collapse --- which poses considerable difficulty to improving them or adapting them to new domains. In this work, we posit that we can remove most such-motivated idiosyncrasies in the pre-training pipelines, and only need to add an explicit coding rate term in the loss function to avoid collapse of the representations. As a result, we obtain highly simplified variants of the DINO and DINOv2 which we call SimDINO and SimDINOv2, respectively. Remarkably, these simplified models are more robust to different design choices, such as network architecture and hyperparameters, and they learn even higher-quality representations, measured by performance on downstream tasks, offering a Pareto improvement over the corresponding DINO and DINOv2 models. This work highlights the potential of using simplifying design principles to improve the empirical practice of deep learning. Code and model checkpoints are available at https://github.com/RobinWu218/SimDINO.
|
Self-supervised learning, DINO
|
We propose a methodology that simplifies and improves DINO, a widely used self-supervised learning algorithm.
| 13,457 |
2502.10385
|
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] |
https://github.com/RobinWu218/SimDINO
|
497 |
Boosting Protein Graph Representations through Static-Dynamic Fusion
|
https://openreview.net/forum?id=QbtBIE36Fd
|
[
"Pengkang Guo",
"Bruno Correia",
"Pierre Vandergheynst",
"Daniel Probst"
] |
Poster
|
Machine learning for protein modeling faces significant challenges due to proteins' inherently dynamic nature, yet most graph-based machine learning methods rely solely on static structural information. Recently, the growing availability of molecular dynamics trajectories provides new opportunities for understanding the dynamic behavior of proteins; however, computational methods for utilizing this dynamic information remain limited. We propose a novel graph representation that integrates both static structural information and dynamic correlations from molecular dynamics trajectories, enabling more comprehensive modeling of proteins. By applying relational graph neural networks (RGNNs) to process this heterogeneous representation, we demonstrate significant improvements over structure-based approaches across three distinct tasks: atomic adaptability prediction, binding site detection, and binding affinity prediction. Our results validate that combining static and dynamic information provides complementary signals for understanding protein-ligand interactions, offering new possibilities for drug design and structural biology applications.
|
Graph Neural Networks, Protein Modeling, Molecular Dynamics, Heterogeneous Graph
| null | 13,452 | null |
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] |
https://github.com/PKGuo/protein-static-dynamic-fusion
|
498 |
Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales
|
https://openreview.net/forum?id=YjBrt82S3v
|
[
"Ju-Seung Byun",
"Andrew Perrault"
] |
Poster
|
Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance. Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) introduce additional challenges. For instance, diverse preferences complicate the alignment process, and prediction errors in a trained reward model can become more severe as the LLM generates unseen outputs. These RL challenges create confusion about whether the probability of an action for a given state should be increased or decreased, similar to the noise in labels for classification tasks. In this work, we focus on RL algorithms that share learning difficulties with cross-entropy loss, especially for low-probability predictions. To enhance stability, we adapt reverse cross-entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss. We demonstrate performance improvements across various tasks and scales. We conduct experiments in discrete action tasks (Atari games) and continuous action space tasks (MuJoCo benchmark and Box2D) using Symmetric A2C (SA2C) and Symmetric PPO (SPPO). Notably, SPPO shows strong performance across different hyperparameters. Furthermore, we validate the symmetric RL loss in the RLHF framework using PPO for natural language processing tasks such as IMDB positive sentiment and TL;DR summarization.
|
Reinforcement learning, Cross entropy, Symmetric loss functions
| null | 13,418 |
2405.17618
|
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] |
https://github.com/shashacks/symmetric_rl
|
499 |
Joint Localization and Activation Editing for Low-Resource Fine-Tuning
|
https://openreview.net/forum?id=Lllg9YjAFX
|
[
"Wen Lai",
"Alexander Fraser",
"Ivan Titov"
] |
Poster
|
Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, are commonly used to adapt LLMs. However, the effectiveness of standard PEFT methods is limited in low-resource scenarios with only a few hundred examples. Recent advances in interpretability research have inspired the emergence of activation editing (or steering) techniques, which modify the activations of specific model components. Due to their extremely small parameter counts, these methods show promise for small datasets. However, their performance is highly dependent on identifying the correct modules to edit and often lacks stability across different datasets. In this paper, we propose Joint Localization and Activation Editing (JoLA), a method that jointly learns (1) which heads in the Transformer to edit (2) whether the intervention should be additive, multiplicative, or both and (3) the intervention parameters themselves - the vectors applied as additive offsets or multiplicative scalings to the head output. Through evaluations on three benchmarks spanning commonsense reasoning, natural language understanding, and natural language generation, we demonstrate that JoLA consistently outperforms existing methods.
|
activation editing, low-resource, localization
|
Activation editing approach that jointly optimizes the selection of intervention components and the intervention strategy in low-resource setting
| 13,402 |
2502.01179
|
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] |
https://github.com/wenlai-lavine/jola
|
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