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200 |
Portable Reward Tuning: Towards Reusable Fine-Tuning across Different Pretrained Models
|
https://openreview.net/forum?id=cYNBsMTAVL
|
[
"Daiki Chijiwa",
"Taku Hasegawa",
"Kyosuke Nishida",
"Kuniko Saito",
"Susumu Takeuchi"
] |
Poster
|
While foundation models have been exploited for various expert tasks with their fine-tuned parameters, any foundation model will be eventually outdated due to its old knowledge or limited capability, and thus should be replaced by a new foundation model. Subsequently, to benefit from its latest knowledge or improved capability, the new foundation model should be fine-tuned on each task again, which incurs not only the additional training cost but also the maintenance cost of the task-specific data. Existing work address this problem by inference-time tuning, i.e., modifying the output probability from the new foundation model by the outputs from the old foundation model and its fine-tuned model, which involves an additional inference cost by the latter two models. In this paper, we explore a new fine-tuning principle (which we call portable reward tuning; PRT) that reduces the inference cost by its nature, based on the reformulation of fine-tuning as the reward maximization with Kullback-Leibler regularization. Specifically, instead of fine-tuning parameters of the foundation models, PRT trains the reward model explicitly through the same loss as in fine-tuning. During inference, the reward model can be used with any foundation model (with the same set of vocabularies or labels) through the formulation of reward maximization. Experimental results, including both vision and language models, show that the PRT-trained model can achieve comparable accuracy with less inference cost, in comparison to the existing work of inference-time tuning.
|
inference-time tuning, reward maximization
| null | 15,647 |
2502.12776
|
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] | null |
201 |
LOB-Bench: Benchmarking Generative AI for Finance - an Application to Limit Order Book Data
|
https://openreview.net/forum?id=CXPpYJpYXQ
|
[
"Peer Nagy",
"Sascha Yves Frey",
"Kang Li",
"Bidipta Sarkar",
"Svitlana Vyetrenko",
"Stefan Zohren",
"Ani Calinescu",
"Jakob Nicolaus Foerster"
] |
Poster
|
While financial data presents one of the most challenging and interesting sequence modelling tasks due to high noise, heavy tails, and strategic interactions, progress in this area has been hindered by the lack of consensus on quantitative evaluation paradigms.
To address this, we present **LOB-Bench**, a benchmark, implemented in python, designed to evaluate the quality and realism of generative message-by-order data for limit order books (LOB) in the LOBSTER format.
Our framework measures distributional differences in conditional and unconditional statistics between generated and real LOB data, supporting flexible multivariate statistical evaluation.
The benchmark also includes features commonly used LOB statistics such as spread, order book volumes, order imbalance, and message inter-arrival times, along with scores from a trained discriminator network. Lastly, LOB-Bench contains "market impact metrics", i.e. the cross-correlations and price response functions for specific events in the data.
We benchmark generative autoregressive state-space models, a (C)GAN, as well as a parametric LOB model and find that the autoregressive GenAI approach beats traditional model classes.
|
finance, generative models, time series, state-space models, benchmark
|
LOB-Bench offers a rigorous framework and open-source Python package for standardized evaluation of generative limit order book data models, addressing evaluation gaps and enhancing model comparisons with quantitative metrics.
| 15,644 | null |
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] |
https://github.com/peernagy/lob_bench
|
202 |
Comparing Comparisons: Informative and Easy Human Feedback with Distinguishability Queries
|
https://openreview.net/forum?id=Cf8gsqWrua
|
[
"Xuening Feng",
"Zhaohui JIANG",
"Timo Kaufmann",
"Eyke Hüllermeier",
"Paul Weng",
"Yifei Zhu"
] |
Poster
|
Learning human objectives from preference feedback has significantly advanced reinforcement learning (RL) in domains where objectives are hard to formalize.
However, traditional methods based on pairwise trajectory comparisons face notable challenges, including the difficulty in comparing trajectories with subtle differences and the limitation of conveying only ordinal information, limiting direct inference of preference strength.
In this paper, we introduce a novel *distinguishability query*, enabling humans to express preference strength by comparing two pairs of trajectories.
Labelers first indicate which of two pairs is easier to distinguish, then provide preference feedback only on the easier pair.
Our proposed query type directly captures preference strength and is expected to reduce the cognitive load on the labeler.
We further connect this query to cardinal utility and difference relations and develop an efficient query selection scheme to achieve a better trade-off between query informativeness and easiness.
Experimental results demonstrate the potential of our method for faster, data-efficient learning and improved user-friendliness in RLHF benchmarks, particularly in classical control settings where preference strength is critical for expected utility maximization.
|
Reinforcement Learning from Human Feedback, Preference-based Reinforcement Learning, Human-in-the-loop Machine Learning
| null | 15,643 | null |
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] | null |
203 |
BounDr.E: Predicting Drug-likeness via Biomedical Knowledge Alignment and EM-like One-Class Boundary Optimization
|
https://openreview.net/forum?id=Z9Xugry05b
|
[
"Dongmin Bang",
"Inyoung Sung",
"Yinhua Piao",
"Sangseon Lee",
"Sun Kim"
] |
Poster
|
The advent of generative AI now enables large-scale $\textit{de novo}$ design of molecules, but identifying viable drug candidates among them remains an open problem. Existing drug-likeness prediction methods often rely on ambiguous negative sets or purely structural features, limiting their ability to accurately classify drugs from non-drugs. In this work, we introduce BounDr.E}: a novel modeling of drug-likeness as a compact space surrounding approved drugs through a dynamic one-class boundary approach. Specifically, we enrich the chemical space through biomedical knowledge alignment, and then iteratively tighten the drug-like boundary by pushing non-drug-like compounds outside via an Expectation-Maximization (EM)-like process. Empirically, BounDr.E achieves 10\% F1-score improvement over the previous state-of-the-art and demonstrates robust cross-dataset performance, including zero-shot toxic compound filtering. Additionally, we showcase its effectiveness through comprehensive case studies in large-scale $\textit{in silico}$ screening. Our codes and constructed benchmark data under various schemes are provided at: https://github.com/eugenebang/boundr_e.
|
Drug-likeness, Expectation-Maximization, Multi-modal learning, AI Drug Discovery
|
Drug-likeness prediction framework based on EM-like one-class boundary optimization with multi-modal alignment of biomedical knowledge graph and structural space.
| 15,634 | null |
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] | null |
204 |
Private Federated Learning using Preference-Optimized Synthetic Data
|
https://openreview.net/forum?id=ZuaU2bYzlc
|
[
"Charlie Hou",
"Mei-Yu Wang",
"Yige Zhu",
"Daniel Lazar",
"Giulia Fanti"
] |
Poster
|
In practical settings, differentially private federated learning (DP-FL) is the dominant method for training models from private, on-device client data. Recent work has suggested that DP-FL may be enhanced or outperformed by methods that use DP synthetic data (Wu et al., 2024; Hou et al., 2024). The primary algorithms for generating DP synthetic data for FL applications require careful prompt engineering based on public information and/or iterative private client feedback. Our key insight is that the private client feedback collected by prior DP synthetic data methods (Hou et al., 2024; Xie et al., 2024) can be viewed as a preference ranking. Our algorithm, Preference Optimization for Private Client Data (POPri) harnesses client feedback using preference optimization algorithms such as Direct Preference Optimization (DPO) to fine-tune LLMs to generate high-quality DP synthetic data. To evaluate POPri, we release LargeFedBench, a new federated text benchmark for uncontaminated LLM evaluations on federated client data. POPri closes the gap in next-token prediction accuracy between the fully-private and non-private settings by up to 68%, compared to 52% for prior synthetic data methods, and 10% for state-of-the-art DP federated learning methods. The code and data are available at https://github.com/meiyuw/POPri.
|
Differential privacy, large language models, synthetic data, federated learning, preference optimization, reinforcement learning
|
We cast private on-device learning (under the synthetic data framework) as an LLM preference optimization problem, and greatly improve the state of the art.
| 15,630 |
2504.16438
|
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] |
https://github.com/meiyuw/POPri
|
205 |
Provable Benefit of Random Permutations over Uniform Sampling in Stochastic Coordinate Descent
|
https://openreview.net/forum?id=KBUSuiLBMq
|
[
"Donghwa Kim",
"Jaewook Lee",
"Chulhee Yun"
] |
Poster
|
We analyze the convergence rates of two popular variants of coordinate descent (CD): random CD (RCD), in which the coordinates are sampled uniformly at random, and random-permutation CD (RPCD), in which random permutations are used to select the update indices. Despite abundant empirical evidence that RPCD outperforms RCD in various tasks, the theoretical gap between the two algorithms’ performance has remained elusive. Even for the benign case of positive-definite quadratic functions with permutation-invariant Hessians, previous efforts have failed to demonstrate a provable performance gap between RCD and RPCD. To this end, we present novel results showing that, for a class of quadratics with permutation-invariant structures, the contraction rate upper bound for RPCD is always strictly smaller than the contraction rate lower bound for RCD for every individual problem instance. Furthermore, we conjecture that this function class contains the worst-case examples of RPCD among all positive-definite quadratics. Combined with our RCD lower bound, this conjecture extends our results to the general class of positive-definite quadratic functions.
|
Optimization, Stochastic Optimization, Coordinate Descent, Random Permutations
|
We prove that using indices from random permutations for coordinate descent (RPCD) is provably faster than using uniformly sampled indices (RCD) for a subclass of positive definite quadratic functions.
| 15,629 |
2505.23152
|
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] | null |
206 |
Optimal and Practical Batched Linear Bandit Algorithm
|
https://openreview.net/forum?id=WcFLasjwXs
|
[
"Sanghoon Yu",
"Min-hwan Oh"
] |
Poster
|
We study the linear bandit problem under limited adaptivity, known as the batched linear bandit. While existing approaches can achieve near-optimal regret in theory, they are often computationally prohibitive or underperform in practice. We propose BLAE, a novel batched algorithm that integrates arm elimination with regularized G-optimal design, achieving the minimax optimal regret (up to logarithmic factors in $T$) in both large-$K$ and small-$K$ regimes for the first time, while using only $O(\log\log T)$ batches. Our analysis introduces new techniques for batch-wise optimal design and refined concentration bounds. Crucially, BLAE demonstrates low computational overhead and strong empirical performance, outperforming state-of-the-art methods in extensive numerical evaluations. Thus, BLAE is the first algorithm to combine provable minimax-optimality in all regimes and practical superiority in batched linear bandits.
|
linear bandit, batched bandit, exploration-exploitation
| null | 15,612 |
2507.08438
|
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] | null |
207 |
SAE-V: Interpreting Multimodal Models for Enhanced Alignment
|
https://openreview.net/forum?id=S4HPn5Bo6k
|
[
"Hantao Lou",
"Changye Li",
"Jiaming Ji",
"Yaodong Yang"
] |
Poster
|
With the integration of image modality, the semantic space of multimodal large language models (MLLMs) is more complex than text-only models, making their interpretability more challenging and their alignment less stable, particularly susceptible to low-quality data, which can lead to inconsistencies between modalities, hallucinations, and biased outputs. As a result, developing interpretability methods for MLLMs is crucial for improving alignment quality and efficiency. In text-only LLMs, Sparse Autoencoders (SAEs) have gained attention for their ability to interpret latent representations. However, extending SAEs to multimodal settings presents new challenges due to modality fusion and the difficulty of isolating cross-modal representations. To address these challenges, we introduce SAE-V, a mechanistic interpretability framework that extends the SAE paradigm to MLLMs. By identifying and analyzing interpretable features along with their corresponding data, SAE-V enables fine-grained interpretation of both model behavior and data quality, facilitating a deeper understanding of cross-modal interactions and alignment dynamics. Moreover, by utilizing cross-modal feature weighting, SAE-V provides an intrinsic data filtering mechanism to enhance model alignment without requiring additional models. Specifically, when applied to the alignment process of MLLMs, SAE-V-based data filtering methods could achieve more than 110% performance with less than 50% data. Our results highlight SAE-V’s ability to enhance interpretability and alignment in MLLMs, providing insights into their internal mechanisms.
|
interpretability, alignment, multimodal large language model
|
We propose SAE-V, a mechanistic interpretability framework for multimodal large language models to analyze multimodal feature and enhance their alignment.
| 15,602 | null |
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] |
https://github.com/PKU-Alignment/SAE-V
|
208 |
OR-Bench: An Over-Refusal Benchmark for Large Language Models
|
https://openreview.net/forum?id=CdFnEu0JZV
|
[
"Justin Cui",
"Wei-Lin Chiang",
"Ion Stoica",
"Cho-Jui Hsieh"
] |
Poster
|
Large Language Models (LLMs) require careful safety alignment to prevent malicious outputs. While significant research focuses on mitigating harmful content generation,
the enhanced safety often come with the side effect of over-refusal, where LLMs may reject innocuous prompts and become less helpful.
Although the issue of over-refusal has been empirically observed, a systematic measurement is challenging
due to the difficulty of crafting prompts that can elicit the over-refusal behaviors of LLMs.
This study proposes a novel method for automatically generating large-scale over-refusal datasets. Leveraging this technique, we introduce OR-Bench, the first large-scale over-refusal benchmark. OR-Bench comprises 80,000 over-refusal prompts across 10 common rejection categories, a subset of around 1,000 hard prompts that are challenging even for state-of-the-art LLMs, and an additional 600 toxic prompts to prevent indiscriminate responses.
We then conduct a comprehensive study to measure the over-refusal of 32 popular LLMs across 8 model families. Our datasets are publicly available at https://huggingface.co/bench-llms and our codebase is open-sourced at https://github.com/justincui03/or-bench.
We hope this benchmark can help the community develop better safety aligned models.
|
safety, llm, over-refusal
|
an over-refusal benchmark for large language model
| 15,599 | null |
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] |
https://github.com/justincui03/or-bench
|
209 |
TRACE Back from the Future: A Probabilistic Reasoning Approach to Controllable Language Generation
|
https://openreview.net/forum?id=LhkSfpfRXW
|
[
"Gwen Yidou Weng",
"Benjie Wang",
"Guy Van den Broeck"
] |
Poster
|
As large language models (LMs) advance, there is an increasing need to control their outputs to align with human values (e.g., detoxification) or desired attributes (e.g., personalization, topic). However, autoregressive models focus on next-token predictions and struggle with global properties that require looking ahead. Existing solutions either post-train LMs for each new attribute—expensive and inflexible—or approximate the Expected Attribute Probability (EAP) of future sequences by sampling or training, which is slow and unreliable for rare attributes. We introduce **TRACE** (Tractable Probabilistic Reasoning for Adaptable Controllable gEneration), a novel framework that efficiently computes EAP and adapts to new attributes through tractable *probabilistic* reasoning and lightweight *control*. TRACE distills a Hidden Markov Model (HMM) from an LM and pairs it with a small classifier to estimate attribute probabilities, enabling exact EAP computation over the HMM’s predicted futures. This EAP is then used to reweigh the LM’s next-token probabilities for globally compliant continuations. Empirically, TRACE achieves state-of-the-art detoxification results with only 20% decoding overhead, yields 76 low-resource personalized LMs within seconds, and seamlessly extends to composite attributes.
|
Controlled Generation, Probabilistic Reasoning, Tractable Inference, Hidden Markov Models (HMM), Detoxification
|
TRACE achieves efficient and adaptable control of language models by using a one-time distilled HMM and a lightweight log-linear classifier to perform exact probabilistic reasoning over future text.
| 15,596 |
2504.18535
|
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] |
https://github.com/yidouweng/trace
|
210 |
AtlasD: Automatic Local Symmetry Discovery
|
https://openreview.net/forum?id=aLDAu7QDw0
|
[
"Manu Bhat",
"Jonghyun Park",
"Jianke Yang",
"Nima Dehmamy",
"Robin Walters",
"Rose Yu"
] |
Poster
|
Existing symmetry discovery methods predominantly focus on global transformations across the entire system or space, but they fail to consider the symmetries in local neighborhoods. This may result in the reported symmetry group being a misrepresentation of the true symmetry. In this paper, we formalize the notion of local symmetry as atlas equivariance. Our proposed pipeline, automatic local symmetry discovery (AtlasD), recovers the local symmetries of a function by training local predictor networks and then learning a Lie group basis to which the predictors are equivariant. We demonstrate AtlasD is capable of discovering local symmetry groups with multiple connected components in top-quark tagging and partial differential equation experiments. The discovered local symmetry is shown to be a useful inductive bias that improves the performance of downstream tasks in climate segmentation and vision tasks. Our code is publicly available at https://github.com/Rose-STL-Lab/AtlasD.
|
Local symmetry discovery, symmetry discovery, equivariance, gauge equivariant neural network, Lie theory
|
A symmetry discovery method capable of learning local transformations.
| 15,595 |
2504.10777
|
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] |
https://github.com/Rose-STL-Lab/AtlasD
|
211 |
Generalization Analysis for Supervised Contrastive Representation Learning under Non-IID Settings
|
https://openreview.net/forum?id=kWSRVtuIuH
|
[
"Nong Minh Hieu",
"Antoine Ledent"
] |
Poster
|
Contrastive Representation Learning (CRL) has achieved impressive success in various domains in recent years. Nevertheless, the theoretical understanding of the generalization behavior of CRL has remained limited. Moreover, to the best of our knowledge, the current literature only analyzes generalization bounds under the assumption that the data tuples used for contrastive learning are independently and identically distributed. However, in practice, we are often limited to a fixed pool of reusable labeled data points, making it inevitable to recycle data across tuples to create sufficiently large datasets. Therefore, the tuple-wise independence condition imposed by previous works is invalidated. In this paper, we provide a generalization analysis for the CRL framework under non-$i.i.d.$ settings that adheres to practice more realistically. Drawing inspiration from the literature on U-statistics, we derive generalization bounds which indicate that the required number of samples in each class scales as the logarithm of the covering number of the class of learnable feature representations associated to that class. Next, we apply our main results to derive excess risk bounds for common function classes such as linear maps and neural networks.
|
Contrastive Learning, Generalization Analysis
|
We derive generalization bounds for CRL when data is limited to a fixed pool of labeled, reusable data points across tuples
| 15,584 |
2505.04937
|
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] | null |
212 |
A Theoretical Justification for Asymmetric Actor-Critic Algorithms
|
https://openreview.net/forum?id=F1yANMCnAn
|
[
"Gaspard Lambrechts",
"Damien Ernst",
"Aditya Mahajan"
] |
Poster
|
In reinforcement learning for partially observable environments, many successful algorithms have been developed within the asymmetric learning paradigm. This paradigm leverages additional state information available at training time for faster learning. Although the proposed learning objectives are usually theoretically sound, these methods still lack a precise theoretical justification for their potential benefits. We propose such a justification for asymmetric actor-critic algorithms with linear function approximators by adapting a finite-time convergence analysis to this setting. The resulting finite-time bound reveals that the asymmetric critic eliminates error terms arising from aliasing in the agent state.
|
Partially Observable Environment, Asymmetric Learning, Privileged Information, Privileged Critic, Convergence Analysis, Asymmetric Actor-Critic, Finite-Time Bound, Agent-State Policy, Aliasing
|
We derive a finite-time bound for an asymmetric actor-critic algorithm and compare it with the symmetric counterpart, offering a justification for the effectiveness of asymmetric learning
| 15,582 |
2501.19116
|
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] | null |
213 |
Reducing Confounding Bias without Data Splitting for Causal Inference via Optimal Transport
|
https://openreview.net/forum?id=fd7ddFBNmP
|
[
"Yuguang Yan",
"Zongyu Li",
"Haolin Yang",
"Zeqin Yang",
"Hao Zhou",
"Ruichu Cai",
"Zhifeng Hao"
] |
Poster
|
Causal inference seeks to estimate the effect given a treatment such as a medicine or the dosage of a medication. To reduce the confounding bias caused by the non-randomized treatment assignment, most existing methods reduce the shift between subpopulations receiving different treatments. However, these methods split limited training samples into smaller groups, which cuts down the number of samples in each group, while precise distribution estimation and alignment highly rely on a sufficient number of training samples. In this paper, we propose a distribution alignment paradigm without data splitting, which can be naturally applied in the settings of binary and continuous treatments. To this end, we characterize the confounding bias by considering different probability measures of the same set including all the training samples, and exploit the optimal transport theory to analyze the confounding bias and outcome estimation error. Based on this, we propose to learn balanced representations by reducing the bias between the marginal distribution and the conditional distribution of a treatment. As a result, data reduction caused by splitting is avoided, and the outcome prediction model trained on one treatment group can be generalized to the entire population. The experiments on both binary and continuous treatment settings demonstrate the effectiveness of our method.
|
Causal inference, continuous treatment, optimal transport
| null | 15,581 | null |
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] | null |
214 |
Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead
|
https://openreview.net/forum?id=6znPjYn11w
|
[
"Won-Jun Jang",
"Hyeon-Seo Park",
"Si-Hyeon Lee"
] |
Poster
|
Federated ensemble distillation addresses client heterogeneity by generating pseudo-labels for an unlabeled server dataset based on client predictions and training the server model using the pseudo-labeled dataset. The unlabeled server dataset can either be pre-existing or generated through a data-free approach. The effectiveness of this approach critically depends on the method of assigning weights to client predictions when creating pseudo-labels, especially in highly heterogeneous settings. Inspired by theoretical results from GANs, we propose a provably near-optimal weighting method that leverages client discriminators trained with a server-distributed generator and local datasets. Our experiments on various image classification tasks demonstrate that the proposed method significantly outperforms baselines. Furthermore, we show that the additional communication cost, client-side privacy leakage, and client-side computational overhead introduced by our method are negligible, both in scenarios with and without a pre-existing server dataset.
|
Federated learning, ensemble distillation, data heterogeneity, generative adversarial network
| null | 15,561 |
2502.06349
|
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] |
https://github.com/pupiu45/FedGO
|
215 |
Ensemble Distribution Distillation via Flow Matching
|
https://openreview.net/forum?id=waeJHU2oeI
|
[
"Jonggeon Park",
"Giung Nam",
"Hyunsu Kim",
"Jongmin Yoon",
"Juho Lee"
] |
Poster
|
Neural network ensembles have proven effective in improving performance across a range of tasks; however, their high computational cost limits their applicability in resource-constrained environments or for large models. Ensemble distillation, the process of transferring knowledge from an ensemble teacher to a smaller student model, offers a promising solution to this challenge. The key is to ensure that the student model is both cost-efficient and achieves performance comparable to the ensemble teacher. With this in mind, we propose a novel ensemble distribution distillation method, which leverages flow matching to effectively transfer the diversity from the ensemble teacher to the student model. Our extensive experiments demonstrate the effectiveness of our proposed method compared to existing ensemble distillation approaches.
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Ensemble Distillation, Flow Matching
|
We distilled ensemble distribution using flow matching
| 15,544 | null |
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] |
https://github.com/Park-Jong-Geon/EDFM
|
216 |
Deep Ridgelet Transform and Unified Universality Theorem for Deep and Shallow Joint-Group-Equivariant Machines
|
https://openreview.net/forum?id=JKsxKPXXUd
|
[
"Sho Sonoda",
"Yuka Hashimoto",
"Isao Ishikawa",
"Masahiro Ikeda"
] |
Poster
|
We present a constructive universal approximation theorem for learning machines equipped with joint-group-equivariant feature maps, called the joint-equivariant machines, based on the group representation theory. ``Constructive'' here indicates that the distribution of parameters is given in a closed-form expression known as the ridgelet transform. Joint-group-equivariance encompasses a broad class of feature maps that generalize classical group-equivariance. Particularly, fully-connected networks are *not* group-equivariant *but* are joint-group-equivariant. Our main theorem also unifies the universal approximation theorems for both shallow and deep networks. Until this study, the universality of deep networks has been shown in a different manner from the universality of shallow networks, but our results discuss them on common ground. Now we can understand the approximation schemes of various learning machines in a unified manner. As applications, we show the constructive universal approximation properties of four examples: depth-$n$ joint-equivariant machine, depth-$n$ fully-connected network, depth-$n$ group-convolutional network, and a new depth-$2$ network with quadratic forms whose universality has not been known.
|
ridgelet transform, group representation, harmonic analysis
| null | 15,537 |
2405.13682
|
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] | null |
217 |
Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors
|
https://openreview.net/forum?id=Ofa1cspTrv
|
[
"Jing Huang",
"Junyi Tao",
"Thomas Icard",
"Diyi Yang",
"Christopher Potts"
] |
Poster
|
Interpretability research now offers a variety of techniques for identifying abstract internal mechanisms in neural networks. Can such techniques be used to predict how models will behave on out-of-distribution examples? In this work, we provide a positive answer to this question. Through a diverse set of language modeling tasks—including symbol manipulation, knowledge retrieval, and instruction following—we show that the most robust features for correctness prediction are those that play a distinctive causal role in the model’s behavior. Specifically, we propose two methods that leverage causal mechanisms to predict the correctness of model outputs: counterfactual simulation (checking whether key causal variables are realized) and value probing (using the values of those variables to make predictions). Both achieve high AUC-ROC in distribution and outperform methods that rely on causal-agnostic features in out-of-distribution settings, where predicting model behaviors is more crucial. Our work thus highlights a novel and significant application for internal causal analysis of language models.
|
Causal Abstraction, Causal Interpretability, OOD, Correctness Prediction
|
Internal causal mechanisms robustly predict language model out-of-distribution behaviors.
| 15,536 |
2505.11770
|
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] |
https://github.com/explanare/ood-prediction
|
218 |
Adversarial Robustness via Deformable Convolution with Stochasticity
|
https://openreview.net/forum?id=vISiVCssVg
|
[
"Yanxiang Ma",
"Zixuan Huang",
"Minjing Dong",
"Shan You",
"Chang Xu"
] |
Poster
|
Random defense represents a promising strategy to protect neural networks from adversarial attacks. Most of these methods enhance robustness by injecting randomness into the data, increasing uncertainty for attackers.
However, this randomness could reduce the generalization capacity of defense, as defense performance could be sensitive to the hyperparameters of noise added to the data, making it difficult to generalize across different datasets.
Additionally, the involvement of randomness always comes with a reduction of natural accuracy, which leads to a delicate trade-off between them, which is seldom studied in random defense. In this work, we propose incorporating randomness into the network structure instead of data input by designing stochastic deformable convolution, where a random mask replaces the convolutional offset. This process promotes data independence, enhancing generalization across datasets. To study the trade-off, we conduct a theoretical analysis of both robust and clean accuracy, from a perspective of gradient cosine similarity and natural inference. Based on the analysis, we reformulate the adversarial training in our random defense framework. Extensive experiments show that our method achieves SOTA adversarial robustness and clean accuracy compared with other random defense methods.
|
Adversarial Robustness; Mechine Learning
|
This paper introduced a random structural adversarial defense method called DCS, inspired by DeConv, and an adaptive AT method for DCS to help it reach and exceed SOTA level.
| 15,534 | null |
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0.004139080177992582
] |
https://github.com/theSleepyPig/Deformable_Convolution_with_Stochasticity
|
219 |
UDora: A Unified Red Teaming Framework against LLM Agents by Dynamically Hijacking Their Own Reasoning
|
https://openreview.net/forum?id=pRmxQHgjb1
|
[
"Jiawei Zhang",
"Shuang Yang",
"Bo Li"
] |
Poster
|
Large Language Model (LLM) agents equipped with external tools have become increasingly powerful for complex tasks such as web shopping, automated email replies, and financial trading. However, these advancements amplify the risks of adversarial attacks, especially when agents can access sensitive external functionalities. Nevertheless, manipulating LLM agents into performing targeted malicious actions or invoking specific tools remains challenging, as these agents extensively reason or plan before executing final actions. In this work, we present UDora, a unified red teaming framework designed for LLM agents that dynamically hijacks the agent's reasoning processes to compel malicious behavior. Specifically, UDora first generates the model’s reasoning trace for the given task, then automatically identifies optimal points within this trace to insert targeted perturbations. The resulting perturbed reasoning is then used as a surrogate response for optimization. By iteratively applying this process, the LLM agent will then be induced to undertake designated malicious actions or to invoke specific malicious tools. Our approach demonstrates superior effectiveness compared to existing methods across three LLM agent datasets. The code is available at https://github.com/AI-secure/UDora.
|
adversarial attack; llm agent; jailbreak; reasoning and acting
|
In this work, we present UDora, a unified red teaming framework designed for LLM Agents that dynamically leverages the agent's own reasoning processes to compel it toward malicious behavior.
| 15,515 |
2503.01908
|
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] |
https://github.com/AI-secure/UDora
|
220 |
Learning Initial Basis Selection for Linear Programming via Duality-Inspired Tripartite Graph Representation and Comprehensive Supervision
|
https://openreview.net/forum?id=WtD8EIzkmm
|
[
"Anqi Lu",
"Junchi Yan"
] |
Poster
|
For the fundamental linear programming (LP) problems, the simplex method remains popular, which usually requires an appropriate initial basis as a warm start to accelerate the solving process. Predicting an initial basis close to an optimal one can often accelerate the solver, but a closer initial basis does not always result in greater acceleration. To achieve better acceleration, we propose a GNN model based on a tripartite graph representation inspired by LP duality. This approach enables more effective feature extraction for general LP problems and enhances the expressiveness of GNNs. Additionally, we introduce novel loss functions targeting basic variable selection and basis feasibility, along with data preprocessing schemes, to further improve learning capability. In addition to achieving high prediction accuracy, we enhance the quality of the initial basis for practical use. Experimental results show that our approach greatly surpasses the state-of-the-art method in predicting initial basis with greater accuracy and in reducing the number of iterations and solving time of the LP solver.
|
supervised learning, graph neural network, linear programming, simplex method, initial basis
| null | 15,510 | null |
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] |
https://github.com/HAHHHD/TripartiteLP
|
221 |
PDUDT: Provable Decentralized Unlearning under Dynamic Topologies
|
https://openreview.net/forum?id=K0Vg8b7nyI
|
[
"Jing Qiao",
"Yu Liu",
"Zengzhe Chen",
"Mingyi Li",
"YUAN YUAN",
"Xiao Zhang",
"Dongxiao Yu"
] |
Poster
|
This paper investigates decentralized unlearning, aiming to eliminate the impact of a specific client on the whole decentralized system. However, decentralized communication characterizations pose new challenges for effective unlearning: the indirect connections make it difficult to trace the specific client's impact, while the dynamic topology limits the scalability of retraining-based unlearning methods.
In this paper, we propose the first **P**rovable **D**ecentralized **U**nlearning algorithm under **D**ynamic **T**opologies called PDUDT. It allows clients to eliminate the influence of a specific client without additional communication or retraining. We provide rigorous theoretical guarantees for PDUDT, showing it is statistically indistinguishable from perturbed retraining. Additionally, it achieves an efficient convergence rate of $\mathcal{O}(\frac{1}{T})$ in subsequent learning, where $T$ is the total communication rounds. This rate matches state-of-the-art results. Experimental results show that compared with the Retrain method, PDUDT saves more than 99\% of unlearning time while achieving comparable unlearning performance.
|
Decentralized unlearning
| null | 15,498 | null |
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] | null |
222 |
Rethinking Score Distilling Sampling for 3D Editing and Generation
|
https://openreview.net/forum?id=1dZgzGTZEO
|
[
"Xingyu Miao",
"Haoran Duan",
"Yang Long",
"Jungong Han"
] |
Poster
|
Score Distillation Sampling (SDS) has emerged as a prominent method for text-to-3D generation by leveraging the strengths of 2D diffusion models. However, SDS is limited to generation tasks and lacks the capability to edit existing 3D assets. Conversely, variants of SDS that introduce editing capabilities often can not generate new 3D assets effectively. In this work, we observe that the processes of generation and editing within SDS and its variants have unified underlying gradient terms. Building on this insight, we propose Unified Distillation Sampling (UDS), a method that seamlessly integrates both the generation and editing of 3D assets. Essentially, UDS refines the gradient terms used in vanilla SDS methods, unifying them to support both tasks. Extensive experiments demonstrate that UDS not only outperforms baseline methods in generating 3D assets with richer details but also excels in editing tasks, thereby bridging the gap between 3D generation and editing.
|
Score Distilling Sampling; 3D Editing and Generation
| null | 15,488 |
2505.01888
|
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] | null |
223 |
Latent Action Learning Requires Supervision in the Presence of Distractors
|
https://openreview.net/forum?id=2gcEQCT7QW
|
[
"Alexander Nikulin",
"Ilya Zisman",
"Denis Tarasov",
"Lyubaykin Nikita",
"Andrei Polubarov",
"Igor Kiselev",
"Vladislav Kurenkov"
] |
Poster
|
Recently, latent action learning, pioneered by Latent Action Policies (LAPO), have shown remarkable pre-training efficiency on observation-only data, offering potential for leveraging vast amounts of video available on the web for embodied AI. However, prior work has focused on distractor-free data, where changes between observations are primarily explained by ground-truth actions. Unfortunately, real-world videos contain action-correlated distractors that may hinder latent action learning. Using Distracting Control Suite (DCS) we empirically investigate the effect of distractors on latent action learning and demonstrate that LAPO struggle in such scenario. We propose LAOM, a simple LAPO modification that improves the quality of latent actions by **8x**, as measured by linear probing. Importantly, we show that providing supervision with ground-truth actions, as few as 2.5% of the full dataset, during latent action learning improves downstream performance by **4.2x** on average. Our findings suggest that integrating supervision during Latent Action Models (LAM) training is critical in the presence of distractors, challenging the conventional pipeline of first learning LAM and only then decoding from latent to ground-truth actions.
|
latent action learning, imitation learning, learning from observations, learning from videos, latent action model
|
We empirically investigate the effect of distractors on latent action learning.
| 15,478 |
2502.00379
|
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] |
https://github.com/dunnolab/laom
|
224 |
SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation Models
|
https://openreview.net/forum?id=nKJGjovmZz
|
[
"Jiawei Zhang",
"Xuan Yang",
"Taiqi Wang",
"Yu Yao",
"Aleksandr Petiushko",
"Bo Li"
] |
Poster
|
Traditional autonomous driving systems often struggle to connect high-level reasoning with low-level control, leading to suboptimal and sometimes unsafe behaviors. Recent advances in multimodal large language models (MLLMs), which process both visual and textual data, offer an opportunity to unify perception and reasoning. However, effectively embedding precise safety knowledge into MLLMs for autonomous driving remains a significant challenge.
To address this, we propose SafeAuto, a framework that enhances MLLM-based autonomous driving by incorporating both unstructured and structured knowledge. First, we introduce a Position-Dependent Cross-Entropy (PDCE) loss to improve low-level control signal predictions when values are represented as text. Second, to explicitly integrate safety knowledge, we develop a reasoning component that translates traffic rules into first-order logic (e.g., "red light => stop") and embeds them into a probabilistic graphical model (e.g., Markov Logic Network) to verify predicted actions using recognized environmental attributes.
Additionally, our Multimodal Retrieval-Augmented Generation (RAG) model leverages video, control signals, and environmental attributes to learn from past driving experiences. Integrating PDCE, MLN, and Multimodal RAG, SafeAuto outperforms existing baselines across multiple datasets, enabling more accurate, reliable, and safer autonomous driving. The code is available at https://github.com/AI-secure/SafeAuto.
|
Autonomous Driving; Multimodal Large Language Models; Multimodal Retrieval-Augmented Generation; Probabilistic Graph Model; Markov Logic Network
|
We propose SafeAuto that includes a specialized PDCE loss for low-level control to improve precision and safety, and enhances high-level action prediction by integrating past driving experiences and precise traffic rules into multimodal models.
| 15,475 |
2503.00211
|
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] |
https://github.com/AI-secure/SafeAuto
|
225 |
Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift
|
https://openreview.net/forum?id=qUTiOeM57J
|
[
"Nguyen Nhat Minh To",
"Paul F R Wilson",
"Viet Nguyen",
"Mohamed Harmanani",
"Michael Cooper",
"Fahimeh Fooladgar",
"Purang Abolmaesumi",
"Parvin Mousavi",
"Rahul Krishnan"
] |
Poster
|
Subpopulation shift, characterized by a disparity in subpopulation distribution between the training and target datasets, can significantly degrade the performance of machine learning models. Current solutions to subpopulation shift involve modifying empirical risk minimization with re-weighting strategies to improve generalization. This strategy relies on assumptions about the number and nature of subpopulations and annotations on group membership, which are unavailable for many real-world datasets. Instead, we propose using an ensemble of diverse classifiers to adaptively capture risk associated with subpopulations. Given a feature extractor network, we replace its standard linear classification layer with a mixture of prototypical classifiers, where each member is trained to classify the data while focusing on different features and samples from other members. In empirical evaluation on nine real-world datasets, covering diverse domains and kinds of subpopulation shift, our method of Diverse Prototypical Ensembles (DPEs) often outperforms the prior state-of-the-art in worst-group accuracy. The code is available at https://github.com/minhto2802/dpe4subpop.
|
distribution shift, subpopulation shift, spurious correlation, class imbalance, attribute imbalance
|
This paper proposes a prototype-based ensemble method to improve robustness against subpopulation shifts without requiring subgroup labels, outperforming state-of-the-art methods across several benchmark datasets.
| 15,471 |
2505.23027
|
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] |
https://github.com/minhto2802/dpe4subpop
|
226 |
Privacy-Shielded Image Compression: Defending Against Exploitation from Vision-Language Pretrained Models
|
https://openreview.net/forum?id=olzs3zVsE7
|
[
"Xuelin Shen",
"Jiayin Xu",
"Kangsheng Yin",
"Wenhan Yang"
] |
Poster
|
The improved semantic understanding of vision-language pretrained (VLP) models has made it increasingly difficult to protect publicly posted images from being exploited by search engines and other similar tools. In this context, this paper seeks to protect users' privacy by implementing defenses at the image compression stage to prevent exploitation. Specifically, we propose a flexible coding method, termed Privacy-Shielded Image Compression (PSIC), that can produce bitstreams with multiple decoding options. By default, the bitstream is decoded to preserve satisfactory perceptual quality while preventing interpretation by VLP models. Our method also retains the original image compression functionality. With a customizable input condition, the proposed scheme can reconstruct the image that preserves its full semantic information. A Conditional Latent Trigger Generation (CLTG) module is proposed to produce bias information based on customizable conditions to guide the decoding process into different reconstructed versions, and an Uncertainty-Aware Encryption-Oriented (UAEO) optimization function is designed to leverage the soft labels inferred from the target VLP model's uncertainty on the training data. This paper further incorporates an adaptive multi-objective optimization strategy to obtain improved encrypting performance and perceptual quality simultaneously within a unified training process. The proposed scheme is plug-and-play and can be seamlessly integrated into most existing Learned Image Compression (LIC) models. Extensive experiments across multiple downstream tasks have demonstrated the effectiveness of our design.
|
Privacy Safeguard, Backdoor Attack, Vision-Language Pretrained Models, Learned Image Compression
| null | 15,467 |
2506.15201
|
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] |
https://github.com/JiayinXu5499/PSIC
|
227 |
HYGMA: Hypergraph Coordination Networks with Dynamic Grouping for Multi-Agent Reinforcement Learning
|
https://openreview.net/forum?id=mgJkeqc685
|
[
"Chiqiang Liu",
"Dazi Li"
] |
Poster
|
Cooperative multi-agent reinforcement learning faces significant challenges in effectively organizing agent relationships and facilitating information exchange, particularly when agents need to adapt their coordination patterns dynamically. This paper presents a novel framework that integrates dynamic spectral clustering with hypergraph neural networks to enable adaptive group formation and efficient information processing in multi-agent systems. The proposed framework dynamically constructs and updates hypergraph structures through spectral clustering on agents' state histories, enabling higher-order relationships to emerge naturally from agent interactions. The hypergraph structure is enhanced with attention mechanisms for selective information processing, providing an expressive and efficient way to model complex agent relationships. This architecture can be implemented in both value-based and policy-based paradigms through a unified objective combining task performance with structural regularization. Extensive experiments on challenging cooperative tasks demonstrate that our method significantly outperforms state-of-the-art approaches in both sample efficiency and final performance. The code is available at: https://github.com/mysteryelder/HYGMA.
|
Multi-Agent Reinforcement Learning, Hypergraph Convolution, Dynamic Grouping, Multi-Agent Cooperation
|
HYGMA integrates dynamic spectral clustering with hypergraph neural networks to enable adaptive agent grouping and efficient information processing in multi-agent reinforcement learning, outperforming state-of-the-art approaches in cooperative tasks.
| 15,465 |
2505.07207
|
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] |
https://github.com/mysteryelder/HYGMA
|
228 |
How Distributed Collaboration Influences the Diffusion Model Training? A Theoretical Perspective
|
https://openreview.net/forum?id=dzGtPrqORu
|
[
"Jing Qiao",
"Yu Liu",
"YUAN YUAN",
"Xiao Zhang",
"Zhipeng Cai",
"Dongxiao Yu"
] |
Poster
|
This paper examines the theoretical performance of distributed diffusion models in environments where computational resources and data availability vary significantly among workers. Traditional models centered on single-worker scenarios fall short in such distributed settings, particularly when some workers are resource-constrained. This discrepancy in resources and data diversity challenges the assumption of accurate score function estimation foundational to single-worker models. We establish the inaugural generation error bound for distributed diffusion models in resource-limited settings, establishing a linear relationship with the data dimension $d$ and consistency with established single-worker results. Our analysis highlights the critical role of hyperparameter selection in influencing the training dynamics, which are key to the performance of model generation. This study provides a streamlined theoretical approach to optimizing distributed diffusion models, paving the way for future research in this area.
|
distributed diffusion models, generation error bound
| null | 15,445 | null |
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] | null |
229 |
A Near Linear Query Lower Bound for Submodular Maximization
|
https://openreview.net/forum?id=LCFPWXymVt
|
[
"Binghui Peng",
"Aviad Rubinstein"
] |
Poster
|
We revisit the problem of selecting $k$-out-of-$n$ elements with the goal of optimizing an objective function, and ask whether it can be solved approximately with sublinear query complexity. For objective functions that are monotone submodular, [Li, Feldman, Kazemi, Karbasi, NeurIPS'22; Kuhnle, AISTATS'21] gave an $\Omega(n/k)$ query lower bound for approximating to within any constant factor. We strengthen their lower bound to a nearly tight $\tilde{\Omega}(n)$. This lower bound holds even for estimating the value of the optimal subset. When the objective function is additive, we prove that finding an approximately optimal subset still requires near-linear query complexity, but we can estimate the value of the optimal subset in $\tilde{O}(n/k)$ queries, and that this is tight up to polylog factors.
|
Submodular maximization, sublinear algorithm
| null | 15,435 | null |
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] | null |
230 |
Local Pan-privacy for Federated Analytics
|
https://openreview.net/forum?id=M18dhHTFf8
|
[
"Vitaly Feldman",
"Audra McMillan",
"Guy N. Rothblum",
"Kunal Talwar"
] |
Poster
|
Pan-privacy was proposed by Dwork et al. (2010) as an approach to designing a private analytics system that retains its privacy properties in the face of intrusions that expose the system's internal state. Motivated by Federated telemetry applications, we study {\em local pan-privacy}, where privacy should be retained under repeated unannounced intrusions {\em on the local state}. We consider the problem of monitoring the count of an event in a federated system, where event occurrences on a local device should be hidden even from an intruder on that device. We show that under reasonable constraints, the goal of providing information-theoretic differential privacy under intrusion is incompatible with collecting telemetry information. We then show that this problem can be solved in a scalable way using standard cryptographic primitives.
|
Differential Privacy, Pan privacy
|
Maintaining differential privacy under intrusion on the local state.
| 15,432 |
2503.11850
|
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] | null |
231 |
Curvature Enhanced Data Augmentation for Regression
|
https://openreview.net/forum?id=l1sx5KiM7Z
|
[
"Ilya Kaufman",
"Omri Azencot"
] |
Poster
|
Deep learning models with a large number of parameters, often referred to as over-parameterized models, have achieved exceptional performance across various tasks. Despite concerns about overfitting, these models frequently generalize well to unseen data, thanks to effective regularization techniques, with data augmentation being among the most widely used. While data augmentation has shown great success in classification tasks using label-preserving transformations, its application in regression problems has received less attention. Recently, a novel manifold learning approach for generating synthetic data was proposed, utilizing a first-order approximation of the data manifold. Building on this foundation, we present a theoretical framework and practical tools for approximating and sampling general data manifolds. Furthermore, we introduce the Curvature-Enhanced Manifold Sampling (CEMS) method for regression tasks. CEMS leverages a second-order representation of the data manifold to enable efficient sampling and reconstruction of new data points. Extensive evaluations across multiple datasets and comparisons with state-of-the-art methods demonstrate that CEMS delivers superior performance in both in-distribution and out-of-distribution scenarios, while introducing only minimal computational overhead. Code is available at https://github.com/azencot-group/CEMS.
|
Manifold learning, Data augmentation, Regression
| null | 15,429 |
2506.06853
|
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] |
https://github.com/azencot-group/CEMS
|
232 |
AdvAgent: Controllable Blackbox Red-teaming on Web Agents
|
https://openreview.net/forum?id=bwidSkOyWF
|
[
"Chejian Xu",
"Mintong Kang",
"Jiawei Zhang",
"Zeyi Liao",
"Lingbo Mo",
"Mengqi Yuan",
"Huan Sun",
"Bo Li"
] |
Poster
|
Foundation model-based agents are increasingly used to automate complex tasks, enhancing efficiency and productivity. However, their access to sensitive resources and autonomous decision-making also introduce significant security risks, where successful attacks could lead to severe consequences. To systematically uncover these vulnerabilities, we propose AdvAgent, a black-box red-teaming framework for attacking web agents. Unlike existing approaches, AdvAgent employs a reinforcement learning-based pipeline to train an adversarial prompter model that optimizes adversarial prompts using feedback from the black-box agent. With careful attack design, these prompts effectively exploit agent weaknesses while maintaining stealthiness and controllability. Extensive evaluations demonstrate that AdvAgent achieves high success rates against state-of-the-art GPT-4-based web agents across diverse web tasks. Furthermore, we find that existing prompt-based defenses provide only limited protection, leaving agents vulnerable to our framework. These findings highlight critical vulnerabilities in current web agents and emphasize the urgent need for stronger defense mechanisms. We release code at https://ai-secure.github.io/AdvAgent/.
|
Web Agents, Large Language Models, Prompt Injection Attack
|
We introduce AdvAgent, a black-box red-teaming framework targeting generalist web agents, leveraging the feedback from target agents to generate adaptive adversarial prompts.
| 15,419 |
2410.17401
|
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] | null |
233 |
The Surprising Effectiveness of Test-Time Training for Few-Shot Learning
|
https://openreview.net/forum?id=asgBo3FNdg
|
[
"Ekin Akyürek",
"Mehul Damani",
"Adam Zweiger",
"Linlu Qiu",
"Han Guo",
"Jyothish Pari",
"Yoon Kim",
"Jacob Andreas"
] |
Poster
|
Language models (LMs) have shown impressive performance on tasks within their training distribution, but often struggle with structurally novel tasks even when given a small number of in-context task examples. We investigate the effectiveness of test-time training (TTT)—temporarily updating model parameters during inference using a loss derived from input data—as a mechanism for improving LMs' reasoning and few-shot learning capabilities. On the Abstraction and Reasoning Corpus (ARC), performing TTT with in-context examples yields up to $6\times$ higher accuracy compared to fine-tuned baselines—reaching $53.0\%$ on the public validation set with an 8B-parameter LM and $61.9\%$ when ensembled with program-synthesis methods, matching average human performance. On BIG-Bench Hard (BBH), TTT on in-context examples surpasses standard few-shot prompting in the $10$-shot setting by $7.3$ percentage points ($50.5\%$ to $57.8\%$). Our findings highlight the limitations of in-context learning for novel tasks and demonstrate the potential of test-time training to enhance language model adaptability.
|
Test-Time Training, In-Context Learning, Few-Shot Learning, ARC, BIG-Bench Hard
|
Test-time training on in-context examples outperforms standard few-shot learning, boosting LM abstract reasoning and adaptability.
| 15,418 |
2411.07279
|
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] |
https://github.com/ekinakyurek
|
234 |
On the Local Complexity of Linear Regions in Deep ReLU Networks
|
https://openreview.net/forum?id=id2CfAgEAk
|
[
"Niket Nikul Patel",
"Guido Montufar"
] |
Poster
|
We define the *local complexity* of a neural network with continuous piecewise linear activations as a measure of the density of linear regions over an input data distribution. We show theoretically that ReLU networks that learn low-dimensional feature representations have a lower local complexity. This allows us to connect recent empirical observations on feature learning at the level of the weight matrices with concrete properties of the learned functions. In particular, we show that the local complexity serves as an upper bound on the total variation of the function over the input data distribution and thus that feature learning can be related to adversarial robustness. Lastly, we consider how optimization drives ReLU networks towards solutions with lower local complexity. Overall, this work contributes a theoretical framework towards relating geometric properties of ReLU networks to different aspects of learning such as feature learning and representation cost.
|
Linear Regions, Adversarial Robustness, Implicit Bias, Representation Learning
| null | 15,410 |
2412.18283
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] | null |
235 |
TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference
|
https://openreview.net/forum?id=8PJmKfeDdp
|
[
"Jack Min Ong",
"Matthew Di Ferrante",
"Aaron Pazdera",
"Ryan Garner",
"Sami Jaghouar",
"Manveer Basra",
"Max Ryabinin",
"Johannes Hagemann"
] |
Poster
|
Large language models (LLMs) have proven to be very capable, but access to frontier models currently relies on inference providers.
This introduces trust challenges: how can we be sure that the provider is using the model configuration they claim?
We propose TOPLOC, a novel method for verifiable inference that addresses this problem.
TOPLOC leverages a compact locality-sensitive hashing mechanism for intermediate activations, which can detect unauthorized modifications to models, prompts, or precision with 100\% accuracy, achieving no false positives or negatives in our empirical evaluations.
Our approach is robust across diverse hardware configurations, GPU types, and algebraic reorderings, which allows for validation speeds significantly faster than the original inference.
By introducing a polynomial encoding scheme, TOPLOC minimizes the memory overhead of the generated proofs by $1000\times$, requiring only 258 bytes of storage per 32 new tokens, compared to the 262 KB requirement of storing the token embeddings directly for Llama 3.1-8B-Instruct.
Our method empowers users to verify LLM inference computations efficiently, fostering greater trust and transparency in open ecosystems and laying a foundation for decentralized, verifiable and trustless AI services.
|
Locality Sensitive Hashing, Large Language Models, Verifiable Computing
|
The paper describes a method to verify LLM inference performed by untrusted compute providers
| 15,401 |
2501.16007
|
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] |
https://github.com/PrimeIntellect-ai/toploc
|
236 |
Exploring Invariance in Images through One-way Wave Equations
|
https://openreview.net/forum?id=HdogAuhlD5
|
[
"Yinpeng Chen",
"Dongdong Chen",
"Xiyang Dai",
"Mengchen Liu",
"Yinan Feng",
"Youzuo Lin",
"Lu Yuan",
"Zicheng Liu"
] |
Poster
|
In this paper, we empirically demonstrate that natural images can be reconstructed with high fidelity from compressed representations using a simple first-order norm-plus-linear autoregressive (FINOLA) process—without relying on explicit positional information. Through systematic analysis, we observe that the learned coefficient matrices ($\mathbf{A}$ and $\mathbf{B}$) in FINOLA are typically invertible, and their product, $\mathbf{AB}^{-1}$, is diagonalizable across training runs. This structure enables a striking interpretation: FINOLA’s latent dynamics resemble a system of one-way wave equations evolving in a compressed latent space. Under this framework, each image corresponds to a unique solution of these equations. This offers a new perspective on image invariance, suggesting that the underlying structure of images may be governed by simple, invariant dynamic laws. Our findings shed light on a novel avenue for understanding and modeling visual data through the lens of latent-space dynamics and wave propagation.
|
Mathematical invariance in images, One-way wave equation, Auto-regression
| null | 15,400 |
2310.12976
|
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] | null |
237 |
Unifying Knowledge from Diverse Datasets to Enhance Spatial-Temporal Modeling: A Granularity-Adaptive Geographical Embedding Approach
|
https://openreview.net/forum?id=uPVynwZxch
|
[
"Zhigaoyuan Wang",
"Ying Sun",
"Hengshu Zhu"
] |
Poster
|
Spatio-temporal forecasting provides potential for discovering evolutionary patterns in geographical scientific data. However, geographical scientific datasets are often manually collected across studies, resulting in limited time spans and data scales. This hinders existing methods that rely on rich historical data for individual entities. In this paper, we argue that heterogeneous datasets from different studies can provide complementary insights into the same underlying system, helping improve predictions for geographical entities with limited historical data. To this end, we propose a Segment Quadtree Geographical Embedding Framework (SQGEF). SQGEF integrates knowledge from datasets with varied target entities, time spans, and observation variables to learn unified representations for multi-granularity entities—including those absent during training. Specifically, we propose a novel data structure, Segment Quadtree, that flexibly accommodates entities of varying granularities. SQGEF not only captures multi-level interactions from grid data but also extracts nested relationships and human-defined boundaries from diverse entities, enabling a comprehensive understanding of complex geographical structures.
Experiments on real-world datasets demonstrate that SQGEF effectively represents unseen geographical entities and enhances performance for various models.
|
Geographical Modeling
| null | 15,394 | null |
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0.023832954466342926,
-0.011303678154945374,
0.019989196211099625,
0.005952479317784309
] | null |
238 |
DiLQR: Differentiable Iterative Linear Quadratic Regulator via Implicit Differentiation
|
https://openreview.net/forum?id=m2EfTrbv4o
|
[
"Shuyuan Wang",
"Philip D Loewen",
"Michael Forbes",
"Bhushan Gopaluni",
"Wei Pan"
] |
Poster
|
While differentiable control has emerged as a powerful paradigm combining model-free flexibility with model-based efficiency, the iterative Linear Quadratic Regulator (iLQR) remains underexplored as a differentiable component. The scalability of differentiating through extended iterations and horizons poses significant challenges, hindering iLQR from being an effective differentiable controller. This paper introduces DiLQR, a framework that facilitates differentiation through iLQR, allowing it to serve as a trainable and differentiable module, either as or within a neural network. A novel aspect of this framework is the analytical solution that it provides for the gradient of an iLQR controller through implicit differentiation, which ensures a constant backward cost regardless of iteration, while producing an accurate gradient. We evaluate our framework on imitation tasks on famous control benchmarks. Our analytical method demonstrates superior computational performance, achieving up to $\textbf{128x}$ speedup and a minimum of $\textbf{21x}$ speedup compared to automatic differentiation. Our method also demonstrates superior learning performance ($\mathbf{10^6x}$) compared to traditional neural network policies and better model loss with differentiable controllers that lack exact analytical gradients. Furthermore, we integrate our module into a larger network with visual inputs to demonstrate the capacity of our method for high-dimensional, fully end-to-end tasks. Codes can be found on the project homepage~\url{https://sites.google.com/view/dilqr/}.
|
iLQR; Differentiable control; Learning based control
| null | 15,377 |
2506.17473
|
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] | null |
239 |
The Disparate Benefits of Deep Ensembles
|
https://openreview.net/forum?id=tjPxZiqeHB
|
[
"Kajetan Schweighofer",
"Adrian Arnaiz-Rodriguez",
"Sepp Hochreiter",
"Nuria M Oliver"
] |
Poster
|
Ensembles of Deep Neural Networks, Deep Ensembles, are widely used as a simple way to boost predictive performance. However, their impact on algorithmic fairness is not well understood yet. Algorithmic fairness examines how a model's performance varies across socially relevant groups defined by protected attributes such as age, gender, or race. In this work, we explore the interplay between the performance gains from Deep Ensembles and fairness. Our analysis reveals that they unevenly favor different groups, a phenomenon that we term the disparate benefits effect. We empirically investigate this effect using popular facial analysis and medical imaging datasets with protected group attributes and find that it affects multiple established group fairness metrics, including statistical parity and equal opportunity. Furthermore, we identify that the per-group differences in predictive diversity of ensemble members can explain this effect. Finally, we demonstrate that the classical Hardt post-processing method is particularly effective at mitigating the disparate benefits effect of Deep Ensembles by leveraging their better-calibrated predictive distributions.
|
Deep Ensembles, Disparate Benefits, Predictive Diversity, Algorithmic Fairness, Post-Processing
|
We uncover the disparate benefits effect of Deep Ensembles, analyze its cause and evaluate approaches to mitigate its negative fairness implications.
| 15,371 |
2410.13831
|
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] |
https://github.com/ml-jku/disparate-benefits
|
240 |
Power Mean Estimation in Stochastic Continuous Monte-Carlo Tree Search
|
https://openreview.net/forum?id=LL8R2QUEvB
|
[
"Tuan Quang Dam"
] |
Poster
|
Monte-Carlo Tree Search (MCTS) has demonstrated success in online planning for deterministic environments, yet significant challenges remain in adapting it to stochastic Markov Decision Processes (MDPs), particularly in continuous state-action spaces. Existing methods, such as HOOT, which combines MCTS with the Hierarchical Optimistic Optimization (HOO) bandit strategy, address continuous spaces but rely on a logarithmic exploration bonus that lacks theoretical guarantees in non-stationary, stochastic settings. Recent advancements, such as Poly-HOOT, introduced a polynomial bonus term to achieve convergence in deterministic MDPs, though a similar theory for stochastic MDPs remains undeveloped. In this paper, we propose a novel MCTS algorithm, Stochastic-Power-HOOT, designed for continuous, stochastic MDPs. Stochastic-Power-HOOT integrates a power mean as a value backup operator, alongside a polynomial exploration bonus to address the non-stationarity inherent in continuous action spaces. Our theoretical analysis establishes that Stochastic-Power-HOOT converges at a polynomial rate of $\mathcal{O}(n^{-1/2})$, where \( n \) is the number of visited trajectories, thereby extending the non-asymptotic convergence guarantees of Poly-HOOT to stochastic environments. Experimental results on synthetic and stochastic tasks validate our theoretical findings, demonstrating the effectiveness of Stochastic-Power-HOOT in continuous, stochastic domains.
|
Monte-Carlo Tree Search; Continuous Reinforcement Learning Planning
|
We develop a theoretically-guaranteed planning algorithm that enables making optimal continuous decisions in unpredictable environments, outperforming existing methods on robotic control tasks.
| 15,370 | null |
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] | null |
241 |
Provable In-Context Vector Arithmetic via Retrieving Task Concepts
|
https://openreview.net/forum?id=DbUmeNnNpt
|
[
"Dake Bu",
"Wei Huang",
"Andi Han",
"Atsushi Nitanda",
"Qingfu Zhang",
"Hau-San Wong",
"Taiji Suzuki"
] |
Poster
|
In-context learning (ICL) has garnered significant attention for its ability to grasp functions/tasks from demonstrations. Recent studies suggest the presence of a latent **task/function vector** in LLMs during ICL. Merullo et al. (2024) showed that LLMs leverage this vector alongside the residual stream for Word2Vec-like vector arithmetic, solving factual-recall ICL tasks. Additionally, recent work empirically highlighted the key role of Question-Answer data in enhancing factual-recall capabilities. Despite these insights, a theoretical explanation remains elusive. To move one step forward, we propose a theoretical framework building on empirically grounded *hierarchical* concept modeling. We develop an optimization theory, showing how nonlinear residual transformers trained via gradient descent on cross-entropy loss perform factual-recall ICL tasks via vector arithmetic. We prove 0-1 loss convergence and show the strong generalization, including robustness to concept recombination and distribution shifts. These results elucidate the advantages of transformers over static embedding predecessors. Empirical simulations corroborate our theoretical insights.
|
Task Vector; Residual Transformer; Factual-recall In-Context Learning;
| null | 15,363 | null |
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] | null |
242 |
Contextual Bandits for Unbounded Context Distributions
|
https://openreview.net/forum?id=gGY9TNVYs3
|
[
"Puning Zhao",
"Rongfei Fan",
"Shaowei Wang",
"Li Shen",
"Qixin Zhang",
"ZongKe",
"Tianhang Zheng"
] |
Poster
|
Nonparametric contextual bandit is an important model of sequential decision making problems. Under $\alpha$-Tsybakov margin condition, existing research has established a regret bound of $\tilde{O}\left(T^{1-\frac{\alpha+1}{d+2}}\right)$ for bounded supports. However, the optimal regret with unbounded contexts has not been analyzed. The challenge of solving contextual bandit problems with unbounded support is to achieve both exploration-exploitation tradeoff and bias-variance tradeoff simultaneously. In this paper, we solve the nonparametric contextual bandit problem with unbounded contexts. We propose two nearest neighbor methods combined with UCB exploration. The first method uses a fixed $k$. Our analysis shows that this method achieves minimax optimal regret under a weak margin condition and relatively light-tailed context distributions. The second method uses adaptive $k$. By a proper data-driven selection of $k$, this method achieves an expected regret of $\tilde{O}\left(T^{1-\frac{(\alpha+1)\beta}{\alpha+(d+2)\beta}}+T^{1-\beta}\right)$, in which $\beta$ is a parameter describing the tail strength. This bound matches the minimax lower bound up to logarithm factors, indicating that the second method is approximately optimal.
|
Contextual bandits, nonparametric statistics
|
Analyze nonparametric contextual bandits
| 15,332 |
2408.09655
|
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] | null |
243 |
PiD: Generalized AI-Generated Images Detection with Pixelwise Decomposition Residuals
|
https://openreview.net/forum?id=gye2zYytx6
|
[
"Xinghe Fu",
"Zhiyuan Yan",
"Zheng Yang",
"Taiping Yao",
"Yandan Zhao",
"Shouhong Ding",
"Xi Li"
] |
Poster
|
Fake images, created by recently advanced generative models, have become increasingly indistinguishable from real ones, making their detection crucial, urgent, and challenging. This paper introduces PiD (Pixelwise Decomposition Residuals), a novel detection method that focuses on residual signals within images. Generative models are designed to optimize high-level semantic content (principal components), often overlooking low-level signals (residual components). PiD leverages this observation by disentangling residual components from images, encouraging the model to uncover more underlying and general forgery clues independent of semantic content. Compared to prior approaches that rely on reconstruction techniques or high-frequency information, PiD is computationally efficient and does not rely on any generative models for reconstruction. Specifically, PiD operates at the pixel level, mapping the pixel vector to another color space (e.g., YUV) and then quantizing the vector. The pixel vector is mapped back to the RGB space and the quantization loss is taken as the residual for AIGC detection. Our experiment results are striking and highly surprising: PiD achieves 98% accuracy on the widely used GenImage benchmark, highlighting the effectiveness and generalization performance.
|
AIGC detection, Generative models, Deepfakes, Deep learning, Image representation, Low-level vision
|
A generalizable AI-generated image detection method with pixelwise decomposition residuals is proposed.
| 15,311 | null |
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] | null |
244 |
CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models
|
https://openreview.net/forum?id=5of0l7eUau
|
[
"Sen Peng",
"Mingyue Wang",
"Jianfei He",
"Jijia Yang",
"Xiaohua Jia"
] |
Poster
|
Latent diffusion models have recently demonstrated superior capabilities in many downstream image synthesis tasks.
However, customization of latent diffusion models using unauthorized data can severely compromise the privacy and intellectual property rights of data owners.
Adversarial examples as protective perturbations have been developed to defend against unauthorized data usage by introducing imperceptible noise to customization samples, preventing diffusion models from effectively learning them.
In this paper, we first reveal that the primary reason adversarial examples are effective as protective perturbations in latent diffusion models is the distortion of their latent representations, as demonstrated through qualitative and quantitative experiments.
We then propose the Contrastive Adversarial Training (CAT) utilizing lightweight adapters as an adaptive attack against these protection methods, highlighting their lack of robustness.
Extensive experiments demonstrate that our CAT method significantly reduces the effectiveness of protective perturbations in customization, urging the community to reconsider and improve the robustness of existing protective perturbations.
The code is available at \url{https://github.com/senp98/CAT}.
|
Latent Diffusion Models, Protective Perturbations, Adversarial Training
|
We reveal the role of latent representation distortion in protective perturbations and propose Contrastive Adversarial Training, an adaptive attack that exposes their robustness weaknesses.
| 15,307 |
2502.07225
|
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] |
https://github.com/senp98/CAT
|
245 |
Online Robust Reinforcement Learning Through Monte-Carlo Planning
|
https://openreview.net/forum?id=m25ma7O7Ec
|
[
"Tuan Quang Dam",
"Kishan Panaganti",
"Brahim Driss",
"Adam Wierman"
] |
Poster
|
Monte Carlo Tree Search (MCTS) is a powerful framework for solving complex decision-making problems, yet it often relies on the assumption that the simulator and the real-world dynamics are identical. Although this assumption helps achieve the success of MCTS in games like Chess, Go, and Shogi, the real-world scenarios incur ambiguity due to their modeling mismatches in low-fidelity simulators. In this work, we present a new robust variant of MCTS that mitigates dynamical model ambiguities. Our algorithm addresses transition dynamics and reward distribution ambiguities to bridge the gap between simulation-based planning and real-world deployment. We incorporate a robust power mean backup operator and carefully designed exploration bonuses to ensure finite-sample convergence at every node in the search tree. We show that our algorithm achieves a convergence rate of $\mathcal{O}(n^{-1/2})$ for the value estimation at the root node, comparable to that of standard MCTS. Finally, we provide empirical evidence that our method achieves robust performance in planning problems even under significant ambiguity in the underlying reward distribution and transition dynamics.
|
Monte-carlo tree search, distributionally robust reinforcement learning, online reinforcement learning
|
we provide monte-carlo tree search based planning algorithm for online robust RL problem
| 15,297 | null |
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] |
https://github.com/brahimdriss/RobustMCTS
|
246 |
Learning Condensed Graph via Differentiable Atom Mapping for Reaction Yield Prediction
|
https://openreview.net/forum?id=sqjQ6p56GR
|
[
"Ankit Ghosh",
"Gargee Kashyap",
"Sarthak Mittal",
"Nupur Jain",
"Raghavan B Sunoj",
"Abir De"
] |
Poster
|
Yield of chemical reactions generally depends on the activation barrier, i.e., the energy difference between the reactant and the transition state. Computing the transition state from the reactant and product graphs requires prior knowledge of the correct node alignment (i.e., atom mapping), which is not available in yield prediction datasets. In this work, we propose YieldNet, a neural yield prediction model, which tackles these challenges. Here, we first approximate the atom mapping between the reactants and products using a differentiable node alignment network. We then use this approximate atom mapping to obtain a noisy realization of the condensed graph of reaction (CGR), which is a supergraph encompassing both the reactants and products. This CGR serves as a surrogate for the transition state graph structure. The CGR embeddings of different steps in a multi-step reaction are then passed into a transformer-guided reaction path encoder.
Our experiments show that YieldNet can predict the yield more accurately than the baselines. Furthermore, the model is trained only under the distant supervision of yield values, without requiring fine-grained supervision of atom mapping.
|
Yield Prediction, GNN, Atom Mapping
|
Learns yield by approximating atom mapping and then a condensed graph of reaction (CGR), where CGR serves as a surrogate of the transition state.
| 15,291 | null |
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] | null |
247 |
What Limits Bidirectional Model's Generative Capabilities? A Uni-Bi-Directional Mixture-of-Expert Method For Bidirectional Fine-tuning
|
https://openreview.net/forum?id=kPqvx2mvec
|
[
"Zuchao Li",
"Yonghua Hei",
"Qiwei Li",
"Lefei Zhang",
"Ping Wang",
"hai zhao",
"Baoyuan Qi",
"Liu Guoming"
] |
Poster
|
Large Language Models (LLMs) excel in generation tasks, yet their causal attention mechanisms limit performance in embedding tasks. While bidirectional modeling may enhance embeddings, naively fine-tuning unidirectional models bidirectionally severely degrades generative performance.
To investigate this trade-off, we analyze attention weights as dependence indicators and find that bidirectional fine-tuning increases subsequent dependence, impairing unidirectional generation. Through systematic Transformer module evaluations, we discover the FFN layer is least affected by such dependence. Leveraging this discovery, we propose UBMoE-LLM, a novel Uni-Bi-directional Mixture-of-Experts LLM, which integrates the original unidirectional FFN with a bidirectionally fine-tuned FFN via unsupervised contrastive learning. This MoE-based approach enhances embedding performance while preserving robust generation.
Extensive experiments across diverse datasets and model scales validate our attention dependence metric and demonstrate UBMoE-LLM’s superior generative quality and reduced hallucination. Code is available at: https://github.com/heiyonghua/ubmoe_llm.
|
Large Language Model, Bidirectional Modeling, Causal Attention
|
We demonstrate that bidirectional training leads to an increase in subsequent dependency, and propose A Uni-Bi-Directional Mixture-of-Expert Large Language Model.
| 15,290 | null |
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] | null |
248 |
The Noisy Laplacian: a Threshold Phenomenon for Non-Linear Dimension Reduction
|
https://openreview.net/forum?id=GK6q2SFNHm
|
[
"Alex Kokot",
"Octavian-Vlad Murad",
"Marina Meila"
] |
Poster
|
In this paper, we clarify the effect of noise on common spectrally
motivated algorithms such as Diffusion Maps (DM) for dimension
reduction. Empirically, these methods are much more robust to noise
than current work suggests. Specifically, existing consistency results
require that either the noise amplitude or dimensionality must vary
with the sample size $n$. We provide new theoretical results
demonstrating that low-frequency eigenpairs reliably capture the
geometry of the underlying manifold under a constant noise level, up to a dimension independent threshold $O(r^{-2})$, where $r$ is the noise amplitude. Our results rely on a decomposition of the manifold Laplacian in the Sasaki
metric, a technique not used before in this area, to our knowledge.
We experimentally validate our theoretical predictions. Additionally, we observe
similar robust behavior for other manifold learning algorithms which
are not based on computing the Laplacian, namely LTSA and VAE.
|
Dimension Reduction, Denoising, Diffusion Maps, Laplacian, VAE, LTSA
|
We show that manifold spectral data can be recovered from noisy samples up to a noise dependent threshold, and investigate the performance of other unsupervised learning algorithms in this setting.
| 15,271 | null |
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] | null |
249 |
GTR: A General, Multi-View, and Dynamic Framework for Trajectory Representation Learning
|
https://openreview.net/forum?id=ehcWKZ2nEn
|
[
"Xiangheng Wang",
"Ziquan Fang",
"Chenglong Huang",
"Danlei Hu",
"Lu Chen",
"Yunjun Gao"
] |
Poster
|
Trajectory representation learning aims to transform raw trajectory data into compact and low-dimensional vectors that are suitable for downstream analysis. However, most existing methods adopt either a free-space view or a road-network view during the learning process, which limits their ability to capture the complex, multi-view spatiotemporal features inherent in trajectory data. Moreover, these approaches rely on task-specific model training, restricting their generalizability and effectiveness for diverse analysis tasks. To this end, we propose GTR, a general, multi-view, and dynamic Trajectory Representation framework built on a pre-train and fine-tune architecture. Specifically, GTR introduces a multi-view encoder that captures the intrinsic multi-view spatiotemporal features. Based on the pre-train and fine-tune architecture, we provide the spatio-temporal fusion pre-training with a spatio-temporal mixture of experts to dynamically combine spatial and temporal features, enabling seamless adaptation to diverse trajectory analysis tasks. Furthermore, we propose an online frozen-hot updating strategy to efficiently update the representation model, accommodating the dynamic nature of trajectory data. Extensive experiments on two real-world datasets demonstrate that GTR consistently outperforms 15 state-of-the-art methods across 6 mainstream trajectory analysis tasks. All source code and data are available at https://github.com/ZJU-DAILY/GTR.
|
Trajectory representation learning, mobility learning, spatio-temporal learning
| null | 15,229 | null |
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] |
https://github.com/ZJU-DAILY/GTR
|
250 |
µnit Scaling: Simple and Scalable FP8 LLM Training
|
https://openreview.net/forum?id=qOLjAhxZgm
|
[
"Saaketh Narayan",
"Abhay Gupta",
"Mansheej Paul",
"Davis Blalock"
] |
Poster
|
Large language model training with 8-bit floating point (FP8) formats promises significant efficiency improvements, but reduced numerical precision makes training challenging. It is currently possible to train in FP8 only if one is willing to tune various hyperparameters, reduce model scale, or accept the overhead of computing dynamic scale factors. We demonstrate simple, scalable FP8 training that requires no dynamic scaling factors or special hyperparameters, even at large model sizes. Our method, \textit{µnit Scaling (µS)}, also enables simple hyperparameter transfer across model widths, matched numerics across training and inference, and other desirable properties. µnit Scaling is straightforward to implement, consisting of a set of minimal interventions based on a first-principles analysis of transformer operations. We validate our method by training models with parameters ranging from 1B to 13B, performing all hidden linear layer computations in FP8. We achieve quality equal to higher-precision baselines while also training up to 33% faster.
|
LLM, FP8, Transformer, Model Training, Attention
|
μnit Scaling combines stable, highly efficient training via FP8 with significant cost savings through hyperparameter transfer.
| 15,192 | null |
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] | null |
251 |
Contextual Optimization Under Model Misspecification: A Tractable and Generalizable Approach
|
https://openreview.net/forum?id=e3NNvqD7wA
|
[
"Omar Bennouna",
"Jiawei Zhang",
"Saurabh Amin",
"Asuman E. Ozdaglar"
] |
Poster
|
Contextual optimization problems are prevalent in decision-making applications where historical data and contextual features are used to learn predictive models that inform optimal actions. However, practical applications often suffer from model misspecification due to incomplete knowledge of the underlying data-generating process, leading to suboptimal decisions. Existing approaches primarily address the well-specified case, leaving a critical gap in handling misspecified models. In this paper, we propose a novel Integrated Learning and Optimization (ILO) framework that explicitly accounts for model misspecification by introducing a tractable surrogate loss function with strong theoretical guarantees on generalizability, tractability, and optimality. Our surrogate loss aligns with the true decision performance objective, ensuring robustness to misspecification without imposing restrictive assumptions. The proposed approach effectively mitigates the challenges of non-convexity and non-smoothness in the target loss function, leading to efficient optimization procedures. We provide rigorous theoretical analysis and experimental validation, demonstrating superior performance compared to state-of-the-art methods. Our work offers a principled solution to the practically relevant challenge of model misspecification in contextual optimization.
|
Contextual Optimization, Machine Learning, Constrained Optimization
| null | 15,188 | null |
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] | null |
252 |
Linear Transformers as VAR Models: Aligning Autoregressive Attention Mechanisms with Autoregressive Forecasting
|
https://openreview.net/forum?id=SxJUV9mnyt
|
[
"Jiecheng Lu",
"Shihao Yang"
] |
Poster
|
Autoregressive attention-based time series forecasting (TSF) has drawn increasing interest, with mechanisms like linear attention often outperforming vanilla attention. However, deeper Transformer architectures frequently misalign with autoregressive objectives, obscuring the underlying VAR structure embedded within linear attention and hindering their ability to capture the data generative processes in TSF. In this work, we first show that a single linear attention layer can be interpreted as a dynamic vector autoregressive (VAR) structure. We then explain that existing multi-layer Transformers have structural mismatches with the autoregressive forecasting objective, which impair interpretability and generalization ability. To address this, we show that by rearranging the MLP, attention, and input-output flow, multi-layer linear attention can also be aligned as a VAR model. Then, we propose Structural Aligned Mixture of VAR (SAMoVAR), a linear Transformer variant that integrates interpretable dynamic VAR weights for multivariate TSF. By aligning the Transformer architecture with autoregressive objectives, SAMoVAR delivers improved performance, interpretability, and computational efficiency, comparing to SOTA TSF models.
|
Linear Attention, Transformer, Time Series Forecasting, Vector Autoregression
| null | 15,182 |
2502.07244
|
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] |
https://github.com/LJC-FVNR/Structural-Aligned-Mixture-of-VAR
|
253 |
NextCoder: Robust Adaptation of Code LMs to Diverse Code Edits
|
https://openreview.net/forum?id=3B6fF1PxYD
|
[
"Tushar Aggarwal",
"Swayam Singh",
"Abhijeet Awasthi",
"Aditya Kanade",
"Nagarajan Natarajan"
] |
Poster
|
Software engineering activities frequently involve edits to existing code. However, contemporary code language models (LMs) lack the ability to handle diverse types of code-edit requirements. In this work, we attempt to overcome this shortcoming through (1) a novel synthetic data generation pipeline and (2) a robust model adaptation algorithm. Starting with seed code examples and diverse editing criteria, our pipeline generates high-quality samples comprising original and modified code, along with natural language instructions in different styles and verbosity. Today's code LMs come bundled with strong abilities, such as code generation and instruction following, which should not be lost due to fine-tuning. To ensure this, we propose a novel adaptation algorithm, SeleKT, that (a) leverages a dense gradient-based step to identify the weights that are most important for code editing, and (b) does a sparse projection onto the base model to avoid overfitting. Using our approach, we obtain a new series of models NextCoder (adapted from QwenCoder-2.5) that achieves strong results on five code-editing benchmarks, outperforming comparable size models and even several larger ones. We show the generality of our approach on two model families DeepSeekCoder and QwenCoder), compare against other fine-tuning approaches, and demonstrate robustness by showing retention of code generation and general problem-solving abilities post adaptation. We opensource the models, synthetic dataset, and implementation at http://aka.ms/nextcoder.
|
Code-LMs, code-editing, code-generation, software engineering
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This paper introduce a synthetic data generation pipeline and a robust model adaptation algorithm to train models for diverse code-editing tasks without losing their original code generation abilities.
| 15,174 | null |
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] | null |
254 |
O-MAPL: Offline Multi-agent Preference Learning
|
https://openreview.net/forum?id=FYvrNKYu6H
|
[
"The Viet Bui",
"Tien Anh Mai",
"Thanh Hong Nguyen"
] |
Poster
|
Inferring reward functions from demonstrations is a key challenge in reinforcement learning (RL), particularly in multi-agent RL (MARL). The large joint state-action spaces and intricate inter-agent interactions in MARL make inferring the joint reward function especially challenging. While prior studies in single-agent settings have explored ways to recover reward functions and expert policies from human preference feedback, such studies in MARL remain limited. Existing methods typically combine two separate stages, supervised reward learning, and standard MARL algorithms, leading to unstable training processes. In this work, we exploit the inherent connection between reward functions and Q functions in cooperative MARL to introduce a novel end-to-end preference-based learning framework.
Our framework is supported by a carefully designed multi-agent value decomposition strategy that enhances training efficiency. Extensive experiments on two state-of-the-art benchmarks, SMAC and MAMuJoCo, using preference data generated by both rule-based and large language model approaches demonstrate that our algorithm consistently outperforms existing methods across various tasks.
|
Multi-agent Reinforcement Learning, Preference Learning
|
Offline Multi-agent Preference Learning
| 15,169 | null |
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] | null |
255 |
RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models
|
https://openreview.net/forum?id=h30EzoI3s0
|
[
"Quan Wei",
"Chung-Yiu Yau",
"Hoi To Wai",
"Yang Zhao",
"Dongyeop Kang",
"Youngsuk Park",
"Mingyi Hong"
] |
Poster
|
Supervised fine-tuning is a standard method for adapting pre-trained large language models (LLMs) to downstream tasks. Quantization has been recently studied as a post-training technique for efficient LLM deployment. To obtain quantized fine-tuned LLMs, conventional pipelines would first fine-tune the pre-trained models, followed by post-training quantization. This often yields suboptimal performance as it fails to leverage the synergy between fine-tuning and quantization. To effectively realize low-bit quantization of weights, activations and KV caches in LLMs, we propose an algorithm named Rotated Straight-Through-Estimator (RoSTE), which combines quantization-aware supervised fine-tuning (QA-SFT) with an adaptive rotation strategy that identifies an effective rotation configuration to reduce activation outliers. We provide theoretical insights on RoSTE by analyzing its prediction error when applied to an overparameterized least square quantized training problem. Our findings reveal that the prediction error is directly proportional to the quantization error of the converged weights, which can be effectively managed through an optimized rotation configuration. Experiments on Pythia, Qwen and Llama models of different sizes demonstrate the effectiveness of RoSTE. Compared to existing post-SFT quantization baselines, our method consistently achieves superior performances across various tasks and different LLM architectures. Our code is available at https://github.com/OptimAI-Lab/RoSTE.
|
model quantization, quantization-aware training, fine-tuning, large language models
| null | 15,164 |
2502.09003
|
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] |
https://github.com/OptimAI-Lab/RoSTE
|
256 |
Reflection-Bench: Evaluating Epistemic Agency in Large Language Models
|
https://openreview.net/forum?id=eff38SdyvN
|
[
"Lingyu Li",
"Yixu Wang",
"Haiquan Zhao",
"Shuqi Kong",
"Yan Teng",
"Chunbo Li",
"Yingchun Wang"
] |
Poster
|
With large language models (LLMs) increasingly deployed as cognitive engines for AI agents, the reliability and effectiveness critically hinge on their intrinsic epistemic agency, which remains understudied. Epistemic agency, the ability to flexibly construct, adapt, and monitor beliefs about dynamic environments, represents a base-model-level capacity independent of specific tools, modules, or applications. We characterize the holistic process underlying epistemic agency, which unfolds in seven interrelated dimensions: prediction, decision-making, perception, memory, counterfactual thinking, belief updating, and meta-reflection. Correspondingly, we propose Reflection-Bench, a cognitive-psychology-inspired benchmark consisting of seven tasks with long-term relevance and minimization of data leakage. Through a comprehensive evaluation of 16 models using three prompting strategies, we identify a clear three-tier performance hierarchy and significant limitations of current LLMs, particularly in meta-reflection capabilities. While state-of-the-art LLMs demonstrate rudimentary signs of epistemic agency, our findings suggest several promising research directions, including enhancing core cognitive functions, improving cross-functional coordination, and developing adaptive processing mechanisms. Our code and data are available at https://github.com/AI45Lab/ReflectionBench.
|
large language models, autonomous agent, cognitive psychology
|
Reflection-Bench is a cognitive psychology inspired, parameterized, and contamination-minimization benchmark that evaluates LLMs' intrinsic epistemic agency, the capacity to flexibly construct, adapt, and monitor beliefs about dynamic environments.
| 15,131 | null |
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] |
https://github.com/AI45Lab/ReflectionBench
|
257 |
COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning
|
https://openreview.net/forum?id=EV0itGFjmm
|
[
"Chamika Sudusinghe",
"Gerasimos Gerogiannis",
"Damitha Lenadora",
"Charles Block",
"Josep Torrellas",
"Charith Mendis"
] |
Poster
|
Sparse tensor programs are essential in deep learning and graph analytics, driving the need for optimized processing. To meet this demand, specialized hardware accelerators are being developed. Optimizing these programs for accelerators is challenging for two reasons: program performance is highly sensitive to variations in sparse inputs, and early-stage accelerators rely on expensive simulators. Therefore, ML-based cost models used for optimizing such programs on general-purpose hardware are often ineffective for early-stage accelerators, as they require large datasets for proper training. To this end, we introduce COGNATE, a novel framework that leverages inexpensive data samples from general-purpose hardware (e.g., CPUs) to train cost models, followed by few-shot fine-tuning on emerging hardware. COGNATE exploits the homogeneity of input features across hardware platforms while effectively mitigating heterogeneity, enabling cost model training with just 5% of the data samples needed by accelerator-specific models to achieve comparable performance. We conduct extensive experiments to demonstrate that COGNATE outperforms existing techniques, achieving average speedups of 1.47× (up to 5.46×) for SpMM and 1.39× (up to 4.22×) for SDDMM.
|
machine learning for systems, transfer learning, hardware accelerators, learned cost models, sparse tensor programs
|
A novel framework that leverages the homogeneity of input features across hardware platforms while mitigating heterogeneity to efficiently fine-tune learned cost models for accelerating sparse tensor programs on emerging hardware platforms.
| 15,114 |
2506.00424
|
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] | null |
258 |
Robot-Gated Interactive Imitation Learning with Adaptive Intervention Mechanism
|
https://openreview.net/forum?id=TC1sQg5z0T
|
[
"Haoyuan Cai",
"Zhenghao Peng",
"Bolei Zhou"
] |
Poster
|
Interactive Imitation Learning (IIL) allows agents to acquire desired behaviors through human interventions, but current methods impose high cognitive demands on human supervisors. We propose the Adaptive Intervention Mechanism (AIM), a novel robot-gated IIL algorithm that learns an adaptive criterion for requesting human demonstrations. AIM utilizes a proxy Q-function to mimic the human intervention rule and adjusts intervention requests based on the alignment between agent and human actions. By assigning high Q-values when the agent deviates from the expert and decreasing these values as the agent becomes proficient, the proxy Q-function enables the agent to assess the real-time alignment with the expert and request assistance when needed. Our expert-in-the-loop experiments reveal that AIM significantly reduces expert monitoring efforts in both continuous and discrete control tasks. Compared to the uncertainty-based baseline Thrifty-DAgger, our method achieves a 40% improvement in terms of human take-over cost and learning efficiency.
Furthermore, AIM effectively identifies safety-critical states for expert assistance, thereby collecting higher-quality expert demonstrations and reducing overall expert data and environment interactions needed. Code and demo video are available at https://github.com/metadriverse/AIM.
|
Imitation Learning, Human-in-the-loop Reinforcement Learning, Shared Autonomy
| null | 15,110 |
2506.09176
|
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] |
https://github.com/metadriverse/AIM
|
259 |
A Reasoning-Based Approach to Cryptic Crossword Clue Solving
|
https://openreview.net/forum?id=kBTgizDiCq
|
[
"Martin Andrews",
"Sam Witteveen"
] |
Poster
|
Cryptic crossword clues are challenging language tasks for which new test sets are released daily by major newspapers on a global basis. Each cryptic clue contains both the definition of the answer to be placed in the crossword grid (in common with regular crosswords), and ‘wordplay’ that *proves* that the answer is correct (i.e. a human solver can be confident that an answer is correct without needing crossing words as confirmation). This work describes an LLM-based reasoning system built from open-licensed components that solves cryptic clues by (i) hypothesising answers; (ii) proposing wordplay explanations; and (iii) using a verifier system that operates on codified reasoning steps. Overall, this system establishes a new state-of-the-art performance on the challenging Cryptonite dataset of clues from The Times and The Telegraph newspapers in the UK. Because each proved solution is expressed in Python, interpretable wordplay reasoning for proven answers is available for inspection.
|
NLP, Cryptic Crosswords, Reasoning, Proof/Verification
|
Our LLM prover/verifier for the reasoning steps in cryptic crossword clues obtains SoTA results
| 15,105 |
2506.04824
|
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] |
https://github.com/mdda/cryptic-crossword-reasoning-verifier
|
260 |
Maximum Update Parametrization and Zero-Shot Hyperparameter Transfer for Fourier Neural Operators
|
https://openreview.net/forum?id=fHt4Nau7FW
|
[
"Shanda Li",
"Shinjae Yoo",
"Yiming Yang"
] |
Poster
|
Fourier Neural Operators (FNOs) offer a principled approach for solving complex partial differential equations (PDEs). However, scaling them to handle more complex PDEs requires increasing the number of Fourier modes, which significantly expands the number of model parameters and makes hyperparameter tuning computationally impractical. To address this, we introduce $\mu$**Transfer-FNO**, a zero-shot hyperparameter transfer technique that enables optimal configurations, tuned on smaller FNOs, to be directly applied to billion-parameter FNOs _without_ additional tuning. Building on the Maximum Update Parametrization ($\mu$P) framework, we mathematically derive a parametrization scheme that facilitates the transfer of optimal hyperparameters across models with different numbers of Fourier modes in FNOs, which is validated through extensive experiments on various PDEs. Our empirical study shows that $\mu$Transfer-FNO reduces computational cost for tuning hyperparameters on large FNOs while maintaining or improving accuracy.
|
Fourier Neural Operators, Hyperparameter transfer
|
We introduce $\mu$Transfer-FNO, a zero-shot hyperparameter transfer method for Fourier Neural Operators that enables efficient hyperparameter tuning by leveraging a novel Maximum Update Parametrization.
| 15,097 | null |
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] |
https://github.com/LithiumDA/muTransfer-FNO
|
261 |
Semantics-aware Test-time Adaptation for 3D Human Pose Estimation
|
https://openreview.net/forum?id=pNZ3pioKRN
|
[
"Qiuxia Lin",
"Rongyu Chen",
"Kerui Gu",
"Angela Yao"
] |
Poster
|
This work highlights a semantics misalignment in 3D human pose estimation. For the task of test-time adaptation, the misalignment manifests as overly smoothed and unguided predictions. The smoothing settles predictions towards some average pose. Furthermore, when there are occlusions or truncations, the adaptation becomes fully unguided. To this end, we pioneer the integration of a semantics-aware motion prior for the test-time adaptation of 3D pose estimation. We leverage video understanding and a well-structured motion-text space to adapt the model motion prediction to adhere to video semantics during test time. Additionally, we incorporate a missing 2D pose completion based on the motion-text similarity. The pose completion strengthens the motion prior's guidance for occlusions and truncations. Our method significantly improves state-of-the-art 3D human pose estimation TTA techniques, with more than 12% decrease in PA-MPJPE on 3DPW and 3DHP.
|
Test-time Adaptation, 3D Human Pose Estimation
| null | 15,096 |
2502.10724
|
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] | null |
262 |
Revisiting Cooperative Off-Policy Multi-Agent Reinforcement Learning
|
https://openreview.net/forum?id=JPkJAyutW0
|
[
"Yueheng Li",
"Guangming Xie",
"Zongqing Lu"
] |
Poster
|
Cooperative Multi-Agent Reinforcement Learning (MARL) has become a critical tool for addressing complex real-world problems.
However, off-policy MARL methods, which rely on joint Q-functions, face significant scalability challenges due to the exponentially growing joint action space.
In this work, we highlight a critical yet often overlooked issue: erroneous Q-target estimation, primarily caused by extrapolation error.
Our analysis reveals that this error becomes increasingly severe as the number of agents grows, leading to unique challenges in MARL due to its expansive joint action space and the decentralized execution paradigm.
To address these challenges, we propose a suite of techniques tailored for off-policy MARL, including annealed multi-step bootstrapping, averaged Q-targets, and restricted action representation. Experimental results demonstrate that these methods effectively mitigate erroneous estimations, yielding substantial performance improvements in challenging benchmarks such as SMAC, SMACv2, and Google Research Football.
|
cooperative multi-agent reinforcement learning, off-policy multi-agent reinforcement learning, value factorization, extrapolation error
| null | 15,095 | null |
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] | null |
263 |
Improving LLM Safety Alignment with Dual-Objective Optimization
|
https://openreview.net/forum?id=Kjivk5OPtL
|
[
"Xuandong Zhao",
"Will Cai",
"Tianneng Shi",
"David Huang",
"Licong Lin",
"Song Mei",
"Dawn Song"
] |
Poster
|
Existing training-time safety alignment techniques for large language models (LLMs) remain vulnerable to jailbreak attacks. Direct preference optimization (DPO), a widely deployed alignment method, exhibits limitations in both experimental and theoretical contexts as its loss function proves suboptimal for refusal learning. Through gradient-based analysis, we identify these shortcomings and propose an improved safety alignment that disentangles DPO objectives into two components: (1) robust refusal training, which encourages refusal even when partial unsafe generations are produced, and (2) targeted unlearning of harmful knowledge. This approach significantly increases LLM robustness against a wide range of jailbreak attacks, including prefilling, suffix, and multi-turn attacks across both in-distribution and out-of-distribution scenarios. Furthermore, we introduce a method to emphasize critical refusal tokens by incorporating a reward-based token-level weighting mechanism for refusal learning, which further improves the robustness against adversarial exploits. Our research also suggests that robustness to jailbreak attacks is correlated with token distribution shifts in the training process and internal representations of refusal and harmful tokens, offering valuable directions for future research in LLM safety alignment. The code is available at https://github.com/wicai24/DOOR-Alignment.
|
LLM Safety, Alignment, DPO
| null | 15,093 |
2503.03710
|
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] |
https://github.com/wicai24/DOOR-Alignment
|
264 |
GuardAgent: Safeguard LLM Agents via Knowledge-Enabled Reasoning
|
https://openreview.net/forum?id=2nBcjCZrrP
|
[
"Zhen Xiang",
"Linzhi Zheng",
"Yanjie Li",
"Junyuan Hong",
"Qinbin Li",
"Han Xie",
"Jiawei Zhang",
"Zidi Xiong",
"Chulin Xie",
"Carl Yang",
"Dawn Song",
"Bo Li"
] |
Poster
|
The rapid advancement of large language model (LLM) agents has raised new concerns regarding their safety and security. In this paper, we propose GuardAgent, the first guardrail agent to protect target agents by dynamically checking whether their actions satisfy given safety guard requests. Specifically, GuardAgent first analyzes the safety guard requests to generate a task plan, and then maps this plan into guardrail code for execution. By performing the code execution, GuardAgent can deterministically follow the safety guard request and safeguard target agents. In both steps, an LLM is utilized as the reasoning component, supplemented by in-context demonstrations retrieved from a memory module storing experiences from previous tasks. In addition, we propose two novel benchmarks: EICU-AC benchmark to assess the access control for healthcare agents and Mind2Web-SC benchmark to evaluate the safety policies for web agents. We show that GuardAgent effectively moderates the violation actions for different types of agents on these two benchmarks with over 98% and 83%
guardrail accuracies, respectively. Project page: https://guardagent.github.io/
|
agent, large language model, guardrail, reasoning
|
We propose GuardAgent, the first LLM agent as a guardrail to protect other LLM agents via knowledge-enabled reasoning.
| 15,081 |
2406.09187
|
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] |
https://github.com/guardagent/code
|
265 |
Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?
|
https://openreview.net/forum?id=ERU7QgD6gc
|
[
"Yujin Han",
"Andi Han",
"Wei Huang",
"Chaochao Lu",
"Difan Zou"
] |
Poster
|
Despite the remarkable success of diffusion models (DMs) in data generation, they exhibit specific failure cases with unsatisfactory outputs. We focus on one such limitation: the ability of DMs to learn hidden rules between image features. Specifically, for image data with dependent features ($\mathbf{x}$) and ($\mathbf{y}$) (e.g., the height of the sun ($\mathbf{x}$) and the length of the shadow ($\mathbf{y}$)), we investigate whether DMs can accurately capture the inter-feature rule ($p(\mathbf{y}|\mathbf{x})$). Empirical evaluations on mainstream DMs (e.g., Stable Diffusion 3.5) reveal consistent failures, such as inconsistent lighting-shadow relationships and mismatched object-mirror reflections. Inspired by these findings, we design four synthetic tasks with strongly correlated features to assess DMs' rule-learning abilities. Extensive experiments show that while DMs can identify coarse-grained rules, they struggle with fine-grained ones. Our theoretical analysis demonstrates that DMs trained via denoising score matching (DSM) exhibit constant errors in learning hidden rules, as the DSM objective is not compatible with rule conformity. To mitigate this, we introduce a common technique - incorporating additional classifier guidance during sampling, which achieves (limited) improvements. Our analysis reveals that the subtle signals of fine-grained rules are challenging for the classifier to capture, providing insights for future exploration.
|
Diffusion Model, Deep Generative Model
| null | 15,070 |
2502.04725
|
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] | null |
266 |
Correlated Errors in Large Language Models
|
https://openreview.net/forum?id=kzYq2hfyHB
|
[
"Elliot Myunghoon Kim",
"Avi Garg",
"Kenny Peng",
"Nikhil Garg"
] |
Poster
|
Diversity in training data, architecture, and providers is assumed to mitigate homogeneity in LLMs. However, we lack empirical evidence on whether different LLMs differ \textit{meaningfully}. We conduct a large-scale empirical evaluation on over 350 LLMs overall, using two popular leaderboards and a resume-screening task. We find substantial correlation in model errors---on one leaderboard dataset, models agree 60\% of the time when both models err. We identify factors driving model correlation, including shared architectures and providers. Crucially, however, larger and more accurate models have highly correlated errors, even with distinct architectures and providers. Finally, we show the effects of correlation in two downstream tasks: LLM-as-judge evaluation and hiring---the latter reflecting theoretical predictions regarding algorithmic monoculture.
|
algorithmic monoculture, evaluations, LLMs
|
We evaluate the extent to which different language models are correlated in how they err.
| 15,069 |
2506.07962
|
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] |
https://github.com/nikhgarg/llm_correlated_errors_public
|
267 |
WAVE: Weighted Autoregressive Varying Gate for Time Series Forecasting
|
https://openreview.net/forum?id=Qqn5ktBUxH
|
[
"Jiecheng Lu",
"Xu Han",
"Yan Sun",
"Shihao Yang"
] |
Poster
|
We propose a Weighted Autoregressive Varying gatE (WAVE) attention mechanism equipped with both Autoregressive (AR) and Moving-average (MA) components. It can adapt to various attention mechanisms, enhancing and decoupling their ability to capture long-range and local temporal patterns in time series data. In this paper, we first demonstrate that, for the time series forecasting (TSF) task, the previously overlooked decoder-only autoregressive Transformer model can achieve results comparable to the best baselines when appropriate tokenization and training methods are applied. Moreover, inspired by the ARMA model from statistics and recent advances in linear attention, we introduce the full ARMA structure into existing autoregressive attention mechanisms. By using an indirect MA weight generation method, we incorporate the MA term while maintaining the time complexity and parameter size of the underlying efficient attention models. We further explore how indirect parameter generation can produce implicit MA weights that align with the modeling requirements for local temporal impacts. Experimental results show that WAVE attention that incorporates the ARMA structure consistently improves the performance of various AR attentions on TSF tasks, achieving state-of-the-art results.
|
Attention, Transformer, Autoregressive Moving-average, Time Series Forecasting
| null | 15,065 |
2410.03159
|
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] |
https://github.com/ljc-fvnr/arma-attention
|
268 |
Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts
|
https://openreview.net/forum?id=dwjwvTwV3V
|
[
"Lan Li",
"Da-Wei Zhou",
"Han-Jia Ye",
"De-Chuan Zhan"
] |
Poster
|
Domain-Incremental Learning (DIL) focuses on continual learning in non-stationary environments, requiring models to adjust to evolving domains while preserving historical knowledge. DIL faces two critical challenges in the context of imbalanced data: intra-domain class imbalance and cross-domain class distribution shifts. These challenges significantly hinder model performance, as intra-domain imbalance leads to underfitting of few-shot classes, while cross-domain shifts require maintaining well-learned many-shot classes and transferring knowledge to improve few-shot class performance in old domains. To overcome these challenges, we introduce the Dual-Balance Collaborative Experts (DCE) framework. DCE employs a frequency-aware expert group, where each expert is guided by specialized loss functions to learn features for specific frequency groups, effectively addressing intra-domain class imbalance. Subsequently, a dynamic expert selector is learned by synthesizing pseudo-features through balanced Gaussian sampling from historical class statistics. This mechanism navigates the trade-off between preserving many-shot knowledge of previous domains and leveraging new data to improve few-shot class performance in earlier tasks. Extensive experimental results on four benchmark datasets demonstrate DCE’s state-of-the-art performance.
|
Domain-incremental learning，Class-imbalanced learning
|
We propose the Dual-Balance Collaborative Experts (DCE) framework to address intra-domain class imbalance and cross-domain class distribution shifts in domain-incremental learning.
| 15,062 |
2507.07100
|
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] |
https://github.com/Lain810/DCE
|
269 |
ReVISE: Learning to Refine at Test-Time via Intrinsic Self-Verification
|
https://openreview.net/forum?id=cBtsxtJqEK
|
[
"Hyunseok Lee",
"Seunghyuk Oh",
"Jaehyung Kim",
"Jinwoo Shin",
"Jihoon Tack"
] |
Poster
|
Self-awareness, i.e., the ability to assess and correct one's generation, is a fundamental aspect of human intelligence, making its replication in large language models (LLMs) an important yet challenging task. Previous works tackle this by employing extensive reinforcement learning or relying on large external verifiers. In this work, we propose Refine via Intrinsic Self-Verification (ReVISE), an efficient and effective framework that enables LLMs to self-correct their outputs through self-verification. The core idea of ReVISE is to enable LLMs to verify their reasoning processes and continually rethink reasoning trajectories based on its verification. To implement this efficiently, we introduce a structured curriculum based on preference learning. Specifically, as ReVISE involves two challenging tasks (i.e., self-verification and reasoning correction), we tackle each task sequentially using curriculum learning, collecting both failed and successful reasoning paths to construct preference pairs for efficient training. During inference, our approach enjoys natural test-time scaling by integrating self-verification and correction capabilities, further enhanced by our proposed confidence-aware decoding mechanism. Our experiments on various reasoning tasks demonstrate that ReVISE achieves efficient self-correction and significantly improves the reasoning performance of LLMs.
|
Self-correct, Test-time compute, Large Language Model, Self-verify, Self-awareness
| null | 15,061 |
2502.14565
|
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] |
https://github.com/seunghyukoh/revise
|
270 |
Large Displacement Motion Transfer with Unsupervised Anytime Interpolation
|
https://openreview.net/forum?id=rMCyR6VSOM
|
[
"Guixiang Wang",
"Jianjun Li"
] |
Poster
|
Motion transfer is to transfer pose in driving video to object of source image, so that object of source image moves. Although great progress has been made recently in unsupervised motion transfer, many unsupervised methods still struggle to accurately model large displacement motions when large motion differences occur between source and driving images. To solve the problem, we propose an unsupervised anytime interpolation based large displacement motion transfer method, which can generate a series of anytime interpolated images between source and driving images. By decomposing large displacement motion into many small displacement motions, difficulty of large displacement motion estimation is reduced. In the process, we design a selector that can select optimal interpolated image from generated interpolated images for downstream tasks. Since there are no real images as labels in the interpolation process, we propose a bidirectional training strategy. Some constraints are added to optimal interpolated image to generate a reasonable interpolated image. To encourage network to generate high-quality images, a pre-trained Vision Transformer model is used to design constraint losses. Finally, experiments show that compared with the large displacement motion between source and driving images, small displacement motion between interpolated and driving images is easier to realize motion transfer. Compared with existing state-of-art methods, our method has significant improvements in motion-related metrics.
|
Image animation, action generation, Unsupervised Interpolation
| null | 15,058 | null |
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] | null |
271 |
One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMs
|
https://openreview.net/forum?id=A31Ep22iQ7
|
[
"Yinghui Li",
"Jiayi Kuang",
"Haojing Huang",
"Zhikun Xu",
"Xinnian Liang",
"Yi Yu",
"Wenlian Lu",
"Yangning Li",
"Xiaoyu Tan",
"Chao Qu",
"Ying Shen",
"Hai-Tao Zheng",
"Philip S. Yu"
] |
Poster
|
Leveraging mathematical Large Language Models (LLMs) for proof generation is a fundamental topic in LLMs research. We argue that the ability of current LLMs to prove statements largely depends on whether they have encountered the relevant proof process during training. This reliance limits their deeper understanding of mathematical theorems and related concepts. Inspired by the pedagogical method of "proof by counterexamples" commonly used in human mathematics education, our work aims to enhance LLMs’ ability to conduct mathematical reasoning and proof through counterexamples. Specifically, we manually create a high-quality, university-level mathematical benchmark, COUNTERMATH, which requires LLMs to prove mathematical statements by providing counterexamples, thereby assessing their grasp of mathematical concepts. Additionally, we develop a data engineering framework to automatically obtain training data for further model improvement. Extensive experiments and detailed analyses demonstrate that COUNTERMATH is challenging, indicating that LLMs, such as OpenAI o1, have insufficient counterexample-driven proof capabilities. Moreover, our exploration into model training reveals that strengthening LLMs' counterexample-driven conceptual reasoning abilities is crucial for improving their overall mathematical capabilities. We believe that our work offers new perspectives on the community of mathematical LLMs.
|
Large Language Models, Mathematical Reasoning
|
The first work aims to enable LLMs to perform mathematical reasoning by giving counterexamples.
| 15,052 |
2502.10454
|
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] |
https://github.com/THUKElab/COUNTERMATH
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272 |
LEAPS: A discrete neural sampler via locally equivariant networks
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https://openreview.net/forum?id=Hq2RniQAET
|
[
"Peter Holderrieth",
"Michael Samuel Albergo",
"Tommi Jaakkola"
] |
Poster
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We propose *LEAPS*, an algorithm to sample from discrete distributions known up to normalization by learning a rate matrix of a continuous-time Markov chain (CTMC). LEAPS can be seen as a continuous-time formulation of annealed importance sampling and sequential Monte Carlo methods, extended so that the variance of the importance weights is offset by the inclusion of the CTMC. To derive these importance weights, we introduce a set of Radon-Nikodym derivatives of CTMCs over their path measures. Because the computation of these weights is intractable with standard neural network parameterizations of rate matrices, we devise a new compact representation for rate matrices via what we call \textit{locally equivariant} functions. To parameterize them, we introduce a family of locally equivariant multilayer perceptrons, attention layers, and convolutional networks, and provide an approach to make deep networks that preserve the local equivariance. This property allows us to propose a scalable training algorithm for the rate matrix such that the variance of the importance weights associated to the CTMC are minimal. We demonstrate the efficacy of LEAPS on problems in statistical physics. We provide code in https://github.com/malbergo/leaps/.
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CTMC, Bayesian inference, MCMC, sampling
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A discrete neural sampler for unnormalized densities via locally equivariant networks.
| 15,045 |
2502.10843
|
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] |
https://github.com/malbergo/leaps
|
273 |
Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery
|
https://openreview.net/forum?id=JvRoF9FRga
|
[
"Ning Liu",
"Yue Yu"
] |
Poster
|
Attention mechanisms have emerged as transformative tools in core AI domains such as natural language processing and computer vision. Yet, their largely untapped potential for modeling intricate physical systems presents a compelling frontier. Learning such systems often entails discovering operators that map between functional spaces using limited instances of function pairs---a task commonly framed as a severely ill-posed inverse PDE problem. In this work, we introduce Neural Interpretable PDEs (NIPS), a novel neural operator architecture that builds upon and enhances Nonlocal Attention Operators (NAO) in both predictive accuracy and computational efficiency. NIPS employs a linear attention mechanism to enable scalable learning and integrates a learnable kernel network that acts as a channel-independent convolution in Fourier space. As a consequence, NIPS eliminates the need to explicitly compute and store large pairwise interactions, effectively amortizing the cost of handling spatial interactions into the Fourier transform. Empirical evaluations demonstrate that NIPS consistently surpasses NAO and other baselines across diverse benchmarks, heralding a substantial leap in scalable, interpretable, and efficient physics learning. Our code and data accompanying this paper are available at https://github.com/fishmoon1234/Nonlocal-Attention-Operator.
|
Neural Operators, Physics Discovery, Attention Mechanism, Inverse PDE problems
| null | 15,041 |
2505.23106
|
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] |
https://github.com/fishmoon1234/Nonlocal-Attention-Operator
|
274 |
MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners
|
https://openreview.net/forum?id=VK47MdCjBH
|
[
"Fang-Duo Tsai",
"Shih-Lun Wu",
"Weijaw Lee",
"Sheng-Ping Yang",
"Bo-Rui Chen",
"Hao-Chung Cheng",
"Yi-Hsuan Yang"
] |
Poster
|
We propose MuseControlLite, a lightweight mechanism designed to fine-tune text-to-music generation models for precise conditioning using various time-varying musical attributes and reference audio signals. The key finding is that positional embeddings, which have been seldom used by text-to-music generation models in the conditioner for text conditions, are critical when the condition of interest is a function of time. Using melody control as an example, our experiments show that simply adding rotary positional embeddings to the decoupled cross-attention layers increases control accuracy from 56.6% to 61.1%, while requiring 6.75 times fewer trainable parameters than state-of-the-art fine-tuning mechanisms, using the same pre-trained diffusion Transformer model of Stable Audio Open. We evaluate various forms of musical attribute control, audio inpainting, and audio outpainting, demonstrating improved controllability over MusicGen-Large and Stable Audio Open ControlNet at a significantly lower fine-tuning cost, with only 85M trainable parameters. Source code, model checkpoints, and demo examples are available at: https://MuseControlLite.github.io/web/
|
Sound, Artificial Intelligence, Machine Learning, Audio and Speech Processing
|
A framework that employs rotary positional embeddings in decoupled cross-attention layers to achieve precise controllability for musical attribute conditioning, as well as audio inpainting and outpainting.
| 15,022 |
2506.18729
|
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] |
https://github.com/fundwotsai2001/MuseControlLite
|
275 |
Diffusion Models are Secretly Exchangeable: Parallelizing DDPMs via Auto Speculation
|
https://openreview.net/forum?id=n08niE37ku
|
[
"Hengyuan Hu",
"Aniket Das",
"Dorsa Sadigh",
"Nima Anari"
] |
Poster
|
Denoising Diffusion Probabilistic Models (DDPMs) have emerged as powerful tools for generative modeling. However, their sequential computation requirements lead to significant inference-time bottlenecks. In this work, we utilize the connection between DDPMs and Stochastic Localization to prove that, under an appropriate reparametrization, the increments of DDPM satisfy an exchangeability property. This general insight enables near-black-box adaptation of various performance optimization techniques from autoregressive models to the diffusion setting. To demonstrate this, we introduce _Autospeculative Decoding_ (ASD), an extension of the widely used speculative decoding algorithm to DDPMs that does not require any auxiliary draft models. Our theoretical analysis shows that ASD achieves a $\tilde{O}(K^{\frac{1}{3}})$ parallel runtime speedup over the $K$ step sequential DDPM. We also demonstrate that a practical implementation of autospeculative decoding accelerates DDPM inference significantly in various domains.
|
DDPM, Stochastic Localization, speculative decoding
| null | 15,015 | null |
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] | null |
276 |
Does Data Scaling Lead to Visual Compositional Generalization?
|
https://openreview.net/forum?id=M2WMUuwoh5
|
[
"Arnas Uselis",
"Andrea Dittadi",
"Seong Joon Oh"
] |
Poster
|
Compositional understanding is crucial for human intelligence, yet it remains unclear whether contemporary vision models exhibit it. The dominant machine learning paradigm is built on the premise that scaling data and model sizes will improve out-of-distribution performance, including compositional generalization. We test this premise through controlled experiments that systematically vary data scale, concept diversity, and combination coverage. We find that compositional generalization is driven by data diversity, not mere data scale. Increased combinatorial coverage forces models to discover a linearly factored representational structure, where concepts decompose into additive components. We prove this structure is key to efficiency, enabling perfect generalization from few observed combinations. Evaluating pretrained models (DINO, CLIP), we find above-random yet imperfect performance, suggesting partial presence of this structure. Our work motivates stronger emphasis on constructing diverse datasets for compositional generalization, and considering the importance of representational structure that enables efficient compositional learning.
|
ML, compositionality, generalization, OOD generalization, scaling, transfer learning
| null | 15,013 |
2507.07102
|
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] |
https://github.com/oshapio/visual-compositional-generalization
|
277 |
NEAR: Neural Electromagnetic Array Response
|
https://openreview.net/forum?id=yXcY4wKAG7
|
[
"Yinyan Bu",
"Jiajie Yu",
"Kai Zheng",
"Xinyu Zhang",
"Piya Pal"
] |
Poster
|
We address the challenge of achieving angular super-resolution in multi-antenna radar systems that are widely used for localization, navigation, and automotive perception. A multi-antenna radar achieves very high resolution by computationally creating a large virtual sensing system using very few physical antennas. However, practical constraints imposed by hardware, noise, and a limited number of antennas can impede its performance. Conventional supervised learning models that rely on extensive pre-training with large datasets, often exhibit poor generalization in unseen environments. To overcome these limitations, we propose NEAR, an untrained implicit neural representation (INR) framework that predicts radar responses at unseen locations from sparse measurements, by leveraging latent harmonic structures inherent in radar wave propagation. We establish new theoretical results linking antenna array response to expressive power of INR architectures, and develop a novel physics-informed and latent geometry-aware regularizer. Our approach integrates classical signal representation with modern implicit neural learning, enabling high-resolution radar sensing that is both interpretable and generalizable. Extensive simulations and real-world experiments using radar platforms demonstrate NEAR's effectiveness and its ability to adapt to unseen environments.
|
Implicit Neural Representation, Untrained Neural Network, Radar Sensing, Super-resolution, Physics-informed Neural Network
| null | 15,009 | null |
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] | null |
278 |
Dynamical phases of short-term memory mechanisms in RNNs
|
https://openreview.net/forum?id=ybBuwgOPOd
|
[
"Bariscan Kurtkaya",
"Fatih Dinc",
"Mert Yuksekgonul",
"Marta Blanco-Pozo",
"Ege Cirakman",
"Mark Schnitzer",
"Yucel Yemez",
"Hidenori Tanaka",
"Peng Yuan",
"Nina Miolane"
] |
Poster
|
Short-term memory is essential for cognitive processing, yet our understanding of its neural mechanisms remains unclear. Neuroscience has long focused on how sequential activity patterns, where neurons fire one after another within large networks, can explain how information is maintained. While recurrent connections were shown to drive sequential dynamics, a mechanistic understanding of this process still remains unknown. In this work, we introduce two unique mechanisms that can support this form of short-term memory: slow-point manifolds generating direct sequences or limit cycles providing temporally localized approximations. Using analytical models, we identify fundamental properties that govern the selection of each mechanism. Precisely, on short-term memory tasks (delayed cue-discrimination tasks), we derive theoretical scaling laws for critical learning rates as a function of the delay period length, beyond which no learning is possible. We empirically verify these results by training and evaluating approximately 80,000 recurrent neural networks (RNNs), which are publicly available for further analysis. Overall, our work provides new insights into short-term memory mechanisms and proposes experimentally testable predictions for systems neuroscience.
|
Dynamical system theory, computational neuroscience, working memory
| null | 15,002 |
2502.17433
|
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] |
https://github.com/fatihdinc/dynamical-phases-stm
|
279 |
Improved Approximations for Hard Graph Problems using Predictions
|
https://openreview.net/forum?id=5QMJZiHuGn
|
[
"Anders Aamand",
"Justin Y. Chen",
"Siddharth Gollapudi",
"Sandeep Silwal",
"Hao WU"
] |
Poster
|
We design improved approximation algorithms for NP-hard graph problems by incorporating predictions (e.g., learned from past data). Our prediction model builds upon and extends the $\varepsilon$-prediction framework by Cohen-Addad, d'Orsi, Gupta, Lee, and Panigrahi (NeurIPS 2024). We consider an edge-based version of this model, where each edge provides two bits of information, corresponding to predictions about whether each of its endpoints belong to an optimal solution. Even with weak predictions where each bit is only $\varepsilon$-correlated with the true solution, this information allows us to break approximation barriers in the standard setting. We develop algorithms with improved approximation ratios for MaxCut, Vertex Cover, Set Cover, and Maximum Independent Set problems (among others). Across these problems, our algorithms share a unifying theme, where we separately satisfy constraints related to high degree vertices (using predictions) and low-degree vertices (without using predictions) and carefully combine the answers.
|
learning-augmented algorithms, learning augmented algorithms, data driven algorithms, data-driven algorithms, combinatorial optimization, approximation algorithms
|
We give learning-augmented approximation algorithms for fundamental NP-Hard graph problems using edge predictions, leading to improved approximation ratios.
| 14,999 |
2505.23967
|
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] | null |
280 |
Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?
|
https://openreview.net/forum?id=I1NtlLvJal
|
[
"Rylan Schaeffer",
"Hailey Schoelkopf",
"Brando Miranda",
"Gabriel Mukobi",
"Varun Madan",
"Adam Ibrahim",
"Herbie Bradley",
"Stella Biderman",
"Sanmi Koyejo"
] |
Poster
|
Predictable behavior from scaling advanced AI systems is an extremely desirable property for engineers, companies, economists and governments alike, and while a well-established literature exists on how pretraining performance scales, predictable scaling behavior on downstream capabilities remains elusive. While many factors are certainly responsible, this paper shines a light on a significant factor that makes predicting scaling behavior on widely used multiple-choice question answering benchmarks challenging and illuminates a path towards making such downstream evaluations predictable with scale. Using five model families and twelve well-established multiple-choice benchmarks, we show that downstream performance is computed from negative log likelihoods via a sequence of transformations that progressively degrades the statistical relationship between performance and scale. We then reveal the mechanism causing this degradation: downstream metrics require comparing the correct choice against a small number of specific incorrect choices, meaning accurately predicting downstream capabilities requires predicting not just how probability mass concentrates on the correct choice with scale, but also how probability mass fluctuates on specific incorrect choices with scale. We empirically study how probability mass on the correct choice co-varies with probability mass on incorrect choices with increasing compute, suggesting that scaling laws for \textit{incorrect} choices might be achievable. Our work also explains why pretraining scaling laws are commonly regarded as more predictable than downstream capabilities and contributes towards establishing scaling-predictable evaluations of frontier AI models.
|
evaluations, benchmarks, scaling laws, emergent abilities, capabilities, frontier models, foundation models
|
What makes predicting downstream capabilities of frontier AI models with scale difficult?
| 14,995 |
2406.04391
|
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] |
https://github.com/RylanSchaeffer/KoyejoLab-Why-Has-Predicting-Downstream-Capabilities-Remained-Elusive
|
281 |
Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback
|
https://openreview.net/forum?id=ATNEHkXFrW
|
[
"Qiwei Di",
"Jiafan He",
"Quanquan Gu"
] |
Poster
|
Learning from human feedback plays an important role in aligning generative models, such as large language models (LLM). However, the effectiveness of this approach can be influenced by adversaries, who may intentionally provide misleading preferences to manipulate the output in an undesirable or harmful direction.
To tackle this challenge, we study a specific model within this problem domain--contextual dueling bandits with adversarial feedback, where the true preference label can be flipped by an adversary. We propose an algorithm namely robust contextual dueling bandits ($\texttt{RCDB}$), which is based on uncertainty-weighted maximum likelihood estimation. Our algorithm achieves an $\tilde O(d\sqrt{T}/\kappa+dC/\kappa)$ regret bound, where $T$ is the number of rounds, $d$ is the dimension of the context, $\kappa$ is the lower bound of the derivative of the link function, and $ 0 \le C \le T$ is the total number of adversarial feedback.
We also prove a lower bound to show that our regret bound is nearly optimal, both in scenarios with and without ($C=0$) adversarial feedback. Our work is the first to achieve nearly minimax optimal regret for dueling bandits in the presence of adversarial preference feedback.
Additionally, for the sigmoid link function, we develop a novel algorithm that takes into account the effect of local derivatives into maximum likelihood estimation (MLE) analysis through a refined method for estimating the link function's derivative. This method helps us to eliminate the $\kappa$ dependence in the leading term with respect to $T$, which reduces the exponential dependence on the parameter radius $B$ to a polynomial dependence. We conduct experiments to evaluate our proposed algorithm $\texttt{RCDB}$ against various types of adversarial feedback. Experimental results demonstrate its superiority over the state-of-the-art dueling bandit algorithms in the presence of adversarial feedback.
|
Dueling Bandits, Adversarial feedback, optimal, uncertainty-weighted maximum likelihood estimation.
|
We study a specific model within this problem domain--contextual dueling bandits with adversarial feedback, where the true preference label can be flipped by an adversary.
| 14,986 |
2404.10776
|
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] | null |
282 |
Contradiction Retrieval via Contrastive Learning with Sparsity
|
https://openreview.net/forum?id=VzFXb6Au58
|
[
"Haike Xu",
"Zongyu Lin",
"Kai-Wei Chang",
"Yizhou Sun",
"Piotr Indyk"
] |
Poster
|
Contradiction retrieval refers to identifying and extracting documents that explicitly disagree with or refute the content of a query, which is important to many downstream applications like fact checking and data cleaning. To retrieve contradiction argument to the query from large document corpora, existing methods such as similarity search and cross-encoder models exhibit different limitations.
To address these challenges, we introduce a novel approach: SparseCL that leverages specially trained sentence embeddings designed to preserve subtle, contradictory nuances between sentences. Our method utilizes a combined metric of cosine similarity and a sparsity function to efficiently identify and retrieve documents that contradict a given query. This approach dramatically enhances the speed of contradiction detection by reducing the need for exhaustive document comparisons to simple vector calculations.
We conduct contradiction retrieval experiments on Arguana, MSMARCO, and HotpotQA, where our method produces an average improvement of $11.0\%$ across different models. We also validate our method on downstream tasks like natural language inference and cleaning corrupted corpora.
This paper outlines a promising direction for non-similarity-based information retrieval which is currently underexplored.
|
contradiction retrieval, sentence embedding
|
We design SparseCL, a novel approach for contradiction retrieval, which leverages specially trained sentence embeddings and a combined metric of cosine similarity and sparsity to efficiently identify documents that contradict a given query.
| 14,984 | null |
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] | null |
283 |
Sounding that Object: Interactive Object-Aware Image to Audio Generation
|
https://openreview.net/forum?id=6KeALGcu2j
|
[
"Tingle Li",
"Baihe Huang",
"Xiaobin Zhuang",
"Dongya Jia",
"Jiawei Chen",
"Yuping Wang",
"Zhuo Chen",
"Gopala Anumanchipalli",
"Yuxuan Wang"
] |
Poster
|
Generating accurate sounds for complex audio-visual scenes is challenging, especially in the presence of multiple objects and sound sources. In this paper, we propose an interactive object-aware audio generation model that grounds sound generation in user-selected visual objects within images. Our method integrates object-centric learning into a conditional latent diffusion model, which learns to associate image regions with their corresponding sounds through multi-modal attention. At test time, our model employs image segmentation to allow users to interactively generate sounds at the object level. We theoretically validate that our attention mechanism functionally approximates test-time segmentation masks, ensuring the generated audio aligns with selected objects. Quantitative and qualitative evaluations show that our model outperforms baselines, achieving better alignment between objects and their associated sounds.
|
Sound Generation, Audio-Visual Learning, Multi-Modal Learning
|
We interactively generate sounds specific to user-selected objects within complex visual scenes.
| 14,971 |
2506.04214
|
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] |
https://github.com/Tinglok/avobject
|
284 |
Correlation Clustering Beyond the Pivot Algorithm
|
https://openreview.net/forum?id=OzQLuoKMQZ
|
[
"Soheil Behnezhad",
"Moses Charikar",
"Vincent Cohen-Addad",
"Alma Ghafari",
"Weiyun ma"
] |
Poster
|
We study the classic correlation clustering problem. Given $n$ objects and a complete labeling of the object-pairs as either “similar” or “dissimilar”, the goal is to partition the objects into
arbitrarily many clusters while minimizing disagreements with
the labels.
A classic Pivot algorithm for this problem, due to [Ailon et al STOC'05], obtains a 3-approximation for this problem. Over the years, this algorithm has been successfully implemented in various settings. The downside of the Pivot algorithm is that the approximation analysis of 3 is tight for it. While better approximations have been achieved in some settings, these algorithms are often hard to implement in various settings. For example, [Behnezhad et al FOCS19] showed that the output of Pivot can be maintained in polylog time per update in a dynamic setting, a bound that was improved to constant by [Dalirrooyfard et al ICML'24]. But obtaining a better approximation remains open.
In this paper, we present Modified Pivot, an algorithm that locally improves the output of Pivot. Our Modified Pivot algorithm can be implemented just as efficiently as Pivot in various settings. Our experiments show that the output of Modified Pivot on average makes less than 77\% of the mistakes made by Pivot. More surprisingly, we prove theoretically that Modified Pivot has approximation ratio $3-\epsilon_0$ for some absolute constant $\epsilon_0 > 0$. This, e.g., leads to a better than 3 approximation in the dynamic setting in polylog time, improving the 3-approximation obtained by [Behnezhad et al FOCS'19] and [Dalirrooyfard et al ICML'24].
|
Correlation Clustering
| null | 14,970 |
2404.06797
|
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] |
https://github.com/almaho/Modified_Pivot_Correlation_Clustering
|
285 |
Test-time Correlation Alignment
|
https://openreview.net/forum?id=0dualJz9OI
|
[
"Linjing You",
"Jiabao Lu",
"Xiayuan Huang"
] |
Poster
|
Deep neural networks often degrade under distribution shifts. Although domain adaptation offers a solution, privacy constraints often prevent access to source data, making Test-Time Adaptation (TTA)—which adapts using only unlabeled test data—increasingly attractive. However, current TTA methods still face practical challenges: (1) a primary focus on instance-wise alignment, overlooking CORrelation ALignment (CORAL) due to missing source correlations; (2) complex backpropagation operations for model updating, resulting in overhead computation and (3) domain forgetting. To address these challenges, we provide a theoretical analysis to investigate the feasibility of **T**est-time **C**orrelation **A**lignment (**TCA**), demonstrating that correlation alignment between high-certainty instances and test instances can enhance test performances with a theoretical guarantee. Based on this, we propose two simple yet effective algorithms: LinearTCA and LinearTCA+. LinearTCA applies a simple linear transformation to achieve both instance and correlation alignment without additional model updates, while LinearTCA+ serves as a plug-and-play module that can easily boost existing TTA methods. Extensive experiments validate our theoretical insights and show that TCA methods significantly outperforms baselines across various tasks, benchmarks and backbones. Notably, LinearTCA achieves higher accuracy with only 4\% GPU memory and 0.6\% computation time compared to the best TTA baseline. It also outperforms existing methods on CLIP over 1.86\%. Code: https://github.com/youlj109/TCA
|
Test-time adaptation, Correlation alignment
|
A theoretically supported TTA paradigm that effectively address the efficiency and domain forgetting chanllenges by aligning feature correlations.
| 14,963 |
2505.00533
|
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] |
https://github.com/youlj109/TCA
|
286 |
BalancEdit: Dynamically Balancing the Generality-Locality Trade-off in Multi-modal Model Editing
|
https://openreview.net/forum?id=JWtcAlXkMN
|
[
"Dongliang Guo",
"Mengxuan Hu",
"Zihan Guan",
"Thomas Hartvigsen",
"Sheng Li"
] |
Poster
|
Large multi-modal models inevitably decay over time as facts update and previously learned information becomes outdated. Traditional approaches such as fine-tuning are often impractical for updating these models due to their size and complexity. Instead, direct knowledge editing within the models presents a more viable solution. Current model editing techniques, however, typically overlook the unique influence ranges of different facts, leading to compromised model performance in terms of both generality and locality. To address this issue, we introduce the concept of the generality-locality trade-off in multi-modal model editing. We develop a new model editing dataset named OKEDIT, specifically designed to effectively evaluate this trade-off. Building on this foundation, we propose \textbf{BalancEdit}, a novel method for balanced model editing that dynamically achieves an optimal balance between generality and locality. BalancEdit utilizes a unique mechanism that generates both positive and negative samples for each fact to accurately determine its influence scope and incorporates these insights into the model's latent space using a discrete, localized codebook of edits, without modifying the underlying model weights. To our knowledge, this is the first approach explicitly addressing the generality-locality trade-off in multi-modal model editing. Our comprehensive results confirm the effectiveness of BalancEdit, demonstrating minimal trade-offs while maintaining robust editing capabilities. Our code and dataset are available at https://github.com/donglgcn/BalancEdit/tree/MMOKVQA.
|
Model editing, Multi-modal model
| null | 14,934 |
2505.01343
|
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] |
https://github.com/donglgcn/BalancEdit/tree/MMOKVQA
|
287 |
DyCodeEval: Dynamic Benchmarking of Reasoning Capabilities in Code Large Language Models Under Data Contamination
|
https://openreview.net/forum?id=3BZyQqbytZ
|
[
"Simin Chen",
"Pranav Pusarla",
"Baishakhi Ray"
] |
Poster
|
The rapid advancement of code large language models (Code LLMs) underscores the critical need for effective and transparent benchmarking methods. However, current benchmarking predominantly relies on publicly available, human-created datasets. The widespread use of these static benchmark datasets makes the evaluation process particularly susceptible to data contamination—an unavoidable consequence of the extensive data collection processes employed during LLM training. Existing methods for addressing data contamination typically face significant limitations, including reliance on substantial human effort and difficulty in managing class imbalances. To overcome these challenges, we propose DyCodeEval, a novel benchmarking suite specifically designed to evaluate Code LLMs under realistic contamination scenarios. Given an initial seed programming problem, DyCodeEval utilizes multiple agents to systematically extract and modify contextual information without changing the core logic, generating semantically equivalent variations. We introduce a dynamic data generation method and conduct extensive empirical studies on two seed datasets involving 18 Code LLMs. The results demonstrate that DyCodeEval effectively assesses the reasoning capabilities of Code LLMs under contamination conditions while producing diverse problem variants, thereby ensuring robust and consistent benchmarking outcomes.
|
benchmarking, code generation, large language model, trustworthy ML
|
We identify the limitations of static benchmarking for code LLMs and propose a dynamic benchmarking approach.
| 14,922 | null |
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] | null |
288 |
ResKoopNet: Learning Koopman Representations for Complex Dynamics with Spectral Residuals
|
https://openreview.net/forum?id=Svk7jjhlSu
|
[
"Yuanchao Xu",
"Kaidi Shao",
"Nikos K. Logothetis",
"Zhongwei Shen"
] |
Poster
|
Analyzing the long-term behavior of high-dimensional nonlinear dynamical systems remains a significant challenge. While the Koopman operator framework provides a powerful global linearization tool, current methods for approximating its spectral components often face theoretical limitations and depend on predefined dictionaries. Residual Dynamic Mode Decomposition (ResDMD) advanced the field by introducing the \emph{spectral residual} to assess Koopman operator approximation accuracy; however, its approach of only filtering precomputed spectra prevents the discovery of the operator's complete spectral information, a limitation known as the `spectral inclusion' problem. We introduce ResKoopNet (Residual-based Koopman-learning Network), a novel method that directly addresses this by explicitly minimizing the \emph{spectral residual} to compute Koopman eigenpairs. This enables the identification of a more precise and complete Koopman operator spectrum. Using neural networks, our approach provides theoretical guarantees while maintaining computational adaptability. Experiments on a variety of physical and biological systems show that ResKoopNet achieves more accurate spectral approximations than existing methods, particularly for high-dimensional systems and those with continuous spectra, which demonstrates its effectiveness as a tool for analyzing complex dynamical systems.
|
Koopman operator, data driven dynamical system, dictionary learning, spectral analysis
| null | 14,918 |
2501.00701
|
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] |
https://github.com/talkingdoll/reskoopnet
|
289 |
Compositional Flows for 3D Molecule and Synthesis Pathway Co-design
|
https://openreview.net/forum?id=4aXfSLfM0Z
|
[
"Tony Shen",
"Seonghwan Seo",
"Ross Irwin",
"Kieran Didi",
"Simon Olsson",
"Woo Youn Kim",
"Martin Ester"
] |
Poster
|
Many generative applications, such as synthesis-based 3D molecular design, involve constructing compositional objects with continuous features.
Here, we introduce Compositional Generative Flows (CGFlow), a novel framework that extends flow matching to generate objects in compositional steps while modeling continuous states.
Our key insight is that modeling compositional state transitions can be formulated as a straightforward extension of the flow matching interpolation process.
We further build upon the theoretical foundations of generative flow networks (GFlowNets), enabling reward-guided sampling of compositional structures.
We apply CGFlow to synthesizable drug design by jointly designing the molecule's synthetic pathway with its 3D binding pose.
Our approach achieves state-of-the-art binding affinity and synthesizability on all 15 targets from the LIT-PCBA benchmark, and 4.2x improvement in sampling efficiency compared to 2D synthesis-based baseline.
To our best knowledge, our method is also the first to achieve state of-art-performance in both Vina Dock (-9.42) and AiZynth success rate (36.1\%) on the CrossDocked2020 benchmark.
|
drug discovery, synthesizable molecular design, GFlowNets, flow matching
|
GFlowNets meet flow matching for 3D molecules generation via synthesis pathways.
| 14,914 |
2504.08051
|
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] | null |
290 |
Aligning Protein Conformation Ensemble Generation with Physical Feedback
|
https://openreview.net/forum?id=Asr955jcuZ
|
[
"Jiarui Lu",
"Xiaoyin Chen",
"Stephen Zhewen Lu",
"Aurelie Lozano",
"Vijil Chenthamarakshan",
"Payel Das",
"Jian Tang"
] |
Poster
|
Protein dynamics play a crucial role in protein biological functions and properties, and their traditional study typically relies on time-consuming molecular dynamics (MD) simulations conducted in silico. Recent advances in generative modeling, particularly denoising diffusion models, have enabled efficient accurate protein structure prediction and conformation sampling by learning distributions over crystallographic structures. However, effectively integrating physical supervision into these data-driven approaches remains challenging, as standard energy-based objectives often lead to intractable optimization. In this paper, we introduce Energy-based Alignment (EBA), a method that aligns generative models with feedback from physical models, efficiently calibrating them to appropriately balance conformational states based on their energy differences. Experimental results on the MD ensemble benchmark demonstrate that EBA achieves state-of-the-art performance in generating high-quality protein ensembles. By improving the physical plausibility of generated structures, our approach enhances model predictions and holds promise for applications in structural biology and drug discovery.
|
Protein, generative models, molecular dynamics, conformation generation, alignments
|
incorporating physical energy feedback into diffusion model for better protein conformation ensemble generation
| 14,909 |
2505.24203
|
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] |
https://github.com/lujiarui/eba
|
291 |
Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models
|
https://openreview.net/forum?id=lNVHg9npif
|
[
"Lucy Xiaoyang Shi",
"brian ichter",
"Michael Robert Equi",
"Liyiming Ke",
"Karl Pertsch",
"Quan Vuong",
"James Tanner",
"Anna Walling",
"Haohuan Wang",
"Niccolo Fusai",
"Adrian Li-Bell",
"Danny Driess",
"Lachy Groom",
"Sergey Levine",
"Chelsea Finn"
] |
Poster
|
Generalist robots that can perform a range of different tasks in open-world settings must be able to not only reason about the steps needed to accomplish their goals, but also process complex instructions, prompts, and even feedback during task execution. Intricate instructions (e.g., "Could you make me a vegetarian sandwich?" or "I don't like that one") require not just the ability to physically perform the individual steps, but the ability to situate complex commands and feedback in the physical world. In this work, we describe a system that uses vision-language models in a hierarchical structure, first reasoning over complex prompts and user feedback to deduce the most appropriate next step to fulfill the task, and then performing that step with low-level actions. In contrast to direct instruction following methods that can fulfill simple commands ("pick up the cup"), our system can reason through complex prompts and incorporate situated feedback during task execution ("that's not trash"). We evaluate our system across three robotic platforms, including single-arm, dual-arm, and dual-arm mobile robots, demonstrating its ability to handle tasks such as cleaning messy tables, making sandwiches, and grocery shopping.
Videos are available at https://www.pi.website/research/hirobot
|
Machine Learning, Robotics, Language, Vision-Language Models
|
Hi Robot enables robots to follow open-ended, complex instructions, adapt to feedback, and interact with humans.
| 14,903 |
2502.19417
|
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] | null |
292 |
Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss
|
https://openreview.net/forum?id=Y0hjl4L1ve
|
[
"Sangyeon Park",
"Isaac Han",
"Seungwon Oh",
"KyungJoong Kim"
] |
Poster
|
Plasticity loss, a critical challenge in neural network training, limits a model's ability to adapt to new tasks or shifts in data distribution. While widely used techniques like L2 regularization and Layer Normalization have proven effective in mitigating this issue, Dropout remains notably ineffective. This paper introduces AID (Activation by Interval-wise Dropout), a novel method inspired by Dropout, designed to address plasticity loss. Unlike Dropout, AID generates subnetworks by applying Dropout with different probabilities on each preactivation interval. Theoretical analysis reveals that AID regularizes the network, promoting behavior analogous to that of deep linear networks, which do not suffer from plasticity loss. We validate the effectiveness of AID in maintaining plasticity across various benchmarks, including continual learning tasks on standard image classification datasets such as CIFAR10, CIFAR100, and TinyImageNet. Furthermore, we show that AID enhances reinforcement learning performance in the Arcade Learning Environment benchmark.
|
loss of plasticity, plasticity, continual learning, reinforcement learning
|
We introduce AID (Activation by Interval-wise Dropout), a novel method inspired by Dropout, designed to address plasticity loss by applying different dropout probabilities across each preactivation interval.
| 14,897 |
2502.01342
|
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] | null |
293 |
Pointwise Information Measures as Confidence Estimators in Deep Neural Networks: A Comparative Study
|
https://openreview.net/forum?id=MPlcU7Sxzs
|
[
"Shelvia Wongso",
"Rohan Ghosh",
"Mehul Motani"
] |
Poster
|
Estimating the confidence of deep neural network predictions is crucial for safe deployment in high-stakes applications. While softmax probabilities are commonly used, they are often poorly calibrated, and existing calibration methods have been shown to be detrimental to failure prediction. In this paper, we propose using information-theoretic measures to estimate prediction confidence in a post-hoc manner, without modifying network architecture or training. Specifically, we compare three pointwise information (PI) measures: pointwise mutual information (PMI), pointwise V-information (PVI), and the recently proposed pointwise sliced mutual information (PSI). These measures are theoretically grounded in their relevance to predictive uncertainty, with properties such as invariance, convergence rates, and sensitivity to geometric attributes like margin and intrinsic dimensionality. Through extensive experiments on benchmark computer vision models and datasets, we find that PVI consistently outperforms or matches existing baselines in post-hoc confidence estimation, excelling over PMI and PSI in both failure prediction and calibration. This aligns with our theoretical insights, which suggest that PVI offers the most balanced trade-offs. Finally, we show that replacing softmax outputs with PVI in existing confidence estimation methods can further enhance performance.
|
information theory, confidence estimation, deep neural networks
| null | 14,884 | null |
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] |
https://github.com/kentridgeai/PI-Conf-Est
|
294 |
FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain
|
https://openreview.net/forum?id=e02oLEbehE
|
[
"Rohan Deb",
"Kiran Koshy Thekumparampil",
"Kousha Kalantari",
"Gaurush Hiranandani",
"Shoham Sabach",
"Branislav Kveton"
] |
Poster
|
Supervised fine-tuning (SFT) is the most common way of adapting large language models (LLMs) to a new domain. In this paper, we improve the efficiency of SFT by selecting an informative subset of training examples. Specifically, for a fixed budget of training examples, which determines the computational cost of fine-tuning, we select those that maximize information gain, as measured by the Fisher information matrix of the SFT objective. We approximate it efficiently by linearization at the last layer of the LLM. Our approach is computationally efficient, analyzable, and performs well empirically. We demonstrate this on several problems, with both quantitative results and LLM-as-a-judge evaluations.
|
active learning, optimal design, supervised fine-tuning, large language models
| null | 14,869 |
2505.14826
|
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] | null |
295 |
Ranking with Multiple Oracles: From Weak to Strong Stochastic Transitivity
|
https://openreview.net/forum?id=d3PjjtGc07
|
[
"Tao Jin",
"Yue Wu",
"Quanquan Gu",
"Farzad Farnoud"
] |
Poster
|
We study the problem of efficiently aggregating the preferences of items from multiple information sources (oracles) and infer the ranking under both the weak stochastic transitivity (WST) and the strong stochastic transitivity (SST) conditions. When the underlying preference model satisfies the WST condition, we propose an algorithm named RMO-WST, which has a bi-level design: at the higher level, it actively allocates comparison budgets to all undetermined pairs until the full ranking is recovered; at the lower level, it attempts to compare the pair of items and selects the more accurate oracles simultaneously. We prove that the sample complexity of RMO-WST is $ \tilde O( N\sum_{i=2}^{N}H_{\sigma^{-1}(i),{\sigma^{-1}(i-1)}} )$, where $N$ is the number of items to rank, $H$ is a problem-dependent hardness factor, and $\sigma^{-1}(i)$ represents the $i$-th best item. We also provide a tight lower bound that matches the upper bound of approximate ranking under the WST condition, answering a previously open problem. In addition, when the SST condition is satisfied, we propose an algorithm named RMO-SST, which can achieve an $\tilde{O}(\sum_{i=1}^{N} H_i \log(N))$ sample complexity. This outperforms the best-known sample complexity by a factor of $\log(N)$. The theoretical advantages of our algorithms are verified by empirical experiments in a simulated environment.
|
active ranking, stochastic transitivity, rank aggregation, dueling bandits, lower bound, upper bound
| null | 14,866 | null |
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] | null |
296 |
RZ-NAS: Enhancing LLM-guided Neural Architecture Search via Reflective Zero-Cost Strategy
|
https://openreview.net/forum?id=9UExQpH078
|
[
"Zipeng Ji",
"Guanghui Zhu",
"Chunfeng Yuan",
"Yihua Huang"
] |
Poster
|
LLM-to-NAS is a promising field at the intersection of Large Language Models (LLMs) and Neural Architecture Search (NAS), as recent research has explored the potential of architecture generation leveraging LLMs on multiple search spaces. However, the existing LLM-to-NAS methods face the challenges of limited search spaces, time-cost search efficiency, and uncompetitive performance across standard NAS benchmarks and multiple downstream tasks. In this work, we propose the Reflective Zero-cost NAS (RZ-NAS) method that can search NAS architectures with humanoid reflections and training-free metrics to elicit the power of LLMs. We rethink LLMs’ roles in NAS in current work and design a structured, prompt-based to comprehensively understand the search tasks and architectures from both text and code levels. By integrating LLM reflection modules, we use LLM-generated feedback to provide linguistic guidance within architecture optimization. RZ-NAS enables effective search within both micro and macro search spaces without extensive time cost, achieving SOTA performance across multiple downstream tasks.
|
Automated Machine Learning, Neural Architecture Search, Large Language Models
|
A novel framework that combines the text- and code-level comprehension capabilities of LLMs with a Reflective Zero-Cost evaluation strategy for NAS
| 14,855 | null |
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] |
https://github.com/PasaLab/RZ-NAS
|
297 |
In-Context Linear Regression Demystified: Training Dynamics and Mechanistic Interpretability of Multi-Head Softmax Attention
|
https://openreview.net/forum?id=3TM3fxwTps
|
[
"Jianliang He",
"Xintian Pan",
"Siyu Chen",
"Zhuoran Yang"
] |
Poster
|
We study how multi-head softmax attention models are trained to perform in-context learning on linear data.
Through extensive empirical experiments and rigorous theoretical analysis, we demystify the emergence of elegant attention patterns: a diagonal and homogeneous pattern in the key-query weights, and a last-entry-only and zero-sum pattern in the output-value weights.
Remarkably, these patterns consistently appear from gradient-based training starting from random initialization.
Our analysis reveals that such emergent structures enable multi-head attention to approximately implement a debiased gradient descent predictor --- one that outperforms single-head attention and nearly achieves Bayesian optimality up to proportional factor.
We also extend our study to scenarios with anisotropic covariates and multi-task linear regression.
Our results reveal that in-context learning ability emerges from the trained transformer as an aggregated effect of its architecture and the underlying data distribution, paving the way for deeper understanding and broader applications of in-context learning.
|
Multi-head Attention, Mechanism Intepretation, Training Dynamics, Expressive Power
| null | 14,853 |
2503.12734
|
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] |
https://github.com/XintianPan/ICL_linear
|
298 |
Triple-Optimistic Learning for Stochastic Contextual Bandits with General Constraints
|
https://openreview.net/forum?id=NhJ4cCifqF
|
[
"Hengquan Guo",
"Lingkai Zu",
"Xin Liu"
] |
Poster
|
We study contextual bandits with general constraints, where a learner observes contexts and aims to maximize cumulative rewards while satisfying a wide range of general constraints.
We introduce the Optimistic$^3$ framework, a novel learning and decision-making approach that integrates optimistic design into parameter learning, primal decision, and dual violation adaptation (i.e., triple-optimism), combined with an efficient primal-dual architecture. Optimistic$^3$ achieves $\tilde{O}(\sqrt{T})$ regret and constraint violation for contextual bandits with general constraints. This framework not only outperforms the state-of-the-art results that achieve $\tilde{O}(T^{\frac{3}{4}})$ guarantees when Slater's condition does not hold but also improves on previous results that achieve $\tilde{O}(\sqrt{T}/\delta)$ when Slater's condition holds ($\delta$ denotes the Slater's condition parameter), offering a $O(1/\delta)$ improvement. Note this improvement is significant because $\delta$ can be arbitrarily small when constraints are particularly challenging.
Moreover, we show that Optimistic$^3$ can be extended to classical multi-armed bandits with both stochastic and adversarial constraints, recovering the best-of-both-worlds guarantee established in the state-of-the-art works, but with significantly less computational overhead.
|
tripe-optimitistic framework, contetxual bandits, general constraints
| null | 14,845 | null |
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] | null |
299 |
Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
|
https://openreview.net/forum?id=TUk7gCqtmf
|
[
"Ruhan Wang",
"Zhiyong Wang",
"Chengkai Huang",
"Rui Wang",
"Tong Yu",
"Lina Yao",
"John C.S. Lui",
"Dongruo Zhou"
] |
Poster
|
For question-answering (QA) tasks, in-context learning (ICL) enables language models (LMs) to generate responses without modifying their parameters by leveraging examples provided in the input. However, the effectiveness of ICL heavily depends on the availability of high-quality examples, which are often scarce due to data privacy constraints, annotation costs, and distribution disparities. A natural solution is to utilize examples stored on client devices, but existing approaches either require transmitting model parameters—incurring significant communication overhead—or fail to fully exploit local datasets, limiting their effectiveness. To address these challenges, we propose Federated In-Context Learning (Fed-ICL), a general framework that enhances ICL through an iterative, collaborative process. Fed-ICL progressively refines responses by leveraging multi-round interactions between clients and a central server, improving answer quality without the need to transmit model parameters. We establish theoretical guarantees for the convergence of Fed-ICL and conduct extensive experiments on standard QA benchmarks, demonstrating that our proposed approach achieves strong performance while maintaining low communication costs.
|
Federated Learning; In-context Learning; Large Language Model; Natural Language Processing
|
We propose Fed-ICL, a communication-efficient and privacy-preserving framework that improves language model performance through federated in-context learning without sharing model parameters or raw data.
| 14,836 |
2506.07440
|
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] | null |
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