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700 |
Cowpox: Towards the Immunity of VLM-based Multi-Agent Systems
|
https://openreview.net/forum?id=5KszXnnkG5
|
[
"YUTONG WU",
"Jie Zhang",
"Yiming Li",
"Chao Zhang",
"Qing Guo",
"Han Qiu",
"Nils Lukas",
"Tianwei Zhang"
] |
Poster
|
Vision Language Model (VLM) Agents are stateful, autonomous entities capable of perceiving and interacting with their environments through vision and language.
Multi-agent systems comprise specialized agents who collaborate to solve a (complex) task.
A core security property is **robustness**, stating that the system maintains its integrity during adversarial attacks.
Multi-agent systems lack robustness, as a successful exploit against one agent can spread and **infect** other agents to undermine the entire system's integrity.
We propose a defense Cowpox to provably enhance the robustness of a multi-agent system by a distributed mechanism that improves the **recovery rate** of agents by limiting the expected number of infections to other agents.
The core idea is to generate and distribute a special *cure sample* that immunizes an agent against the attack before exposure.
We demonstrate the effectiveness of Cowpox empirically and provide theoretical robustness guarantees.
|
VLM, Adversarial Examples, Infectious Attack
|
We build an mechanism to granted the VLM-based multi-agent system with the specific immunity against the infectious jailbreaking attack
| 12,309 | null |
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] | null |
701 |
FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
|
https://openreview.net/forum?id=JfLgvNe1tj
|
[
"Sucheng Ren",
"Qihang Yu",
"Ju He",
"Xiaohui Shen",
"Alan Yuille",
"Liang-Chieh Chen"
] |
Poster
|
Autoregressive (AR) modeling has achieved remarkable success in natural language processing by enabling models to generate text with coherence and contextual understanding through next token prediction. Recently, in image generation, VAR proposes scale-wise autoregressive modeling, which extends the next token prediction to the next scale prediction, preserving the 2D structure of images. However, VAR encounters two primary challenges: (1) its complex and rigid scale design limits generalization in next scale prediction, and (2) the generator’s dependence on a discrete tokenizer with the same complex scale structure restricts modularity and flexibility in updating the tokenizer. To address these limitations, we introduce FlowAR, a general next scale prediction method featuring a streamlined scale design, where each subsequent scale is simply double the previous one. This eliminates the need for VAR’s intricate multi-scale residual tokenizer and enables the use of any off-the-shelf Variational AutoEncoder (VAE). Our simplified design enhances generalization in next scale prediction and facilitates the integration of Flow Matching for high-quality image synthesis. We validate the effectiveness of FlowAR on the challenging ImageNet-256 benchmark, demonstrating superior generation performance compared to previous methods. Codes is available at \href{https://github.com/OliverRensu/FlowAR}{https://github.com/OliverRensu/FlowAR}.
|
Image synthesis and generation
| null | 12,306 |
2412.15205
|
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] |
https://github.com/OliverRensu/FlowAR
|
702 |
SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression
|
https://openreview.net/forum?id=4UfRP8MopP
|
[
"Mohammad Mozaffari",
"Amir Yazdanbakhsh",
"Maryam Mehri Dehnavi"
] |
Poster
|
Conventional model compression techniques for LLMs address high memory consumption and slow inference challenges but typically require computationally expensive retraining to preserve accuracy. In contrast, one-shot compression methods eliminate retraining cost, but struggle to achieve accuracy comparable to dense models. This paper presents SLIM, a new one-shot compression framework that holistically integrates hardware-friendly quantization, sparsity, and low-rank approximation into a unified process. First, we formulate the quantization process using a probabilistic approach (SLIM-Quant) that enables us to apply uniform quantization. Then, we use an existing one-shot pruning method to apply semi-structured sparsity on top of the quantized weights. Finally, to compensate for the introduced aggregated quantization and sparsity error, we use a novel saliency function with unique invertible and additive features that enables us to
mathematically compute the value of low-rank adapters. SLIM improves model accuracy by up to 5.66% (LLaMA-2-7B) for 2:4 sparsity with 4-bit weight quantization, outperforming prior methods. Models compressed with SLIM achieve up to 4.3× and 3.8× on Nvidia RTX3060 and A100 GPUs, respectively. Additionally, they achieve up to 0.23× end-to-end memory reduction in comparison to their dense counterparts. We also propose an optional PEFT recipe that further improves accuracy
by up to 1.66% (LLaMA-2-13B) compared to SLIM without fine-tuning.
|
sparsity, 2:4 sparsity, quantization, low-rank, lora
|
We introduce SLiM, a one-shot quantized sparse plus low-rank method for compressing LLMs without retraining, reducing memory and inference costs while improving the accuracy, offering efficient deployment in memory-constrained environments.
| 12,301 |
2410.09615
|
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] |
https://github.com/Mohammad-Mozaffari/slim
|
703 |
Softmax is not Enough (for Sharp Size Generalisation)
|
https://openreview.net/forum?id=S4JmmpnSPy
|
[
"Petar Veličković",
"Christos Perivolaropoulos",
"Federico Barbero",
"Razvan Pascanu"
] |
Poster
|
A key property of reasoning systems is the ability to make sharp decisions on their input data. For contemporary AI systems, a key carrier of sharp behaviour is the softmax function, with its capability to perform differentiable query-key lookups. It is a common belief that the predictive power of networks leveraging softmax arises from "circuits" which sharply perform certain kinds of computations consistently across many diverse inputs. However, for these circuits to be robust, they would need to generalise well to arbitrary valid inputs. In this paper, we dispel this myth: even for tasks as simple as finding the maximum key, any learned circuitry must disperse as the number of items grows at test time. We attribute this to a fundamental limitation of the softmax function to robustly approximate sharp functions with increasing problem size, prove this phenomenon theoretically, and propose adaptive temperature as an ad-hoc technique for improving the sharpness of softmax at inference time.
|
softmax, size generalisation, attention, transformers, length generalisation, sharpness, entropy
|
We prove that the softmax function cannot robustly model sharp functions with increasing size, based on several controlled experimental observations over simple attention heads, as well as in language models.
| 12,291 |
2410.01104
|
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] | null |
704 |
TAROT: Targeted Data Selection via Optimal Transport
|
https://openreview.net/forum?id=EznrK7QWgK
|
[
"Lan Feng",
"Fan Nie",
"Yuejiang Liu",
"Alexandre Alahi"
] |
Poster
|
We propose TAROT, a targeted data selection framework grounded in Optimal Transport theory. Previous targeted data selection methods primarily rely on influence-based greedy heuristics to enhance domain-specific performance. While effective on limited, unimodal data (i.e., data following a single pattern), these methods struggle as target data complexity increases. Specifically, in multimodal distributions, such heuristics fail to account for multiple inherent patterns, leading to suboptimal data selection. This work identifies two primary limitations: (i) the disproportionate impact of dominant feature components in high-dimensional influence estimation, and (ii) the restrictive linear additive assumptions in greedy selection strategies. To address these challenges, TAROT incorporates whitened feature distance to mitigate dominant feature bias, offering a more reliable measure of data influence. Building on this, TAROT leverages whitened feature distance to quantify and minimize the optimal transport distance between selected data and target domains. Notably, this minimization also facilitates the estimation of optimal selection ratios. We evaluate TAROT across multiple tasks, including semantic segmentation, motion prediction, and instruction tuning. Results consistently show that TAROT outperforms state-of-the-art methods, demonstrating its versatility across various deep learning tasks. Code is available at: https://github.com/vita-epfl/TAROT.
|
Data Attribution, Data Selection, Optimal Transport
| null | 12,283 |
2412.00420
|
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] |
https://github.com/vita-epfl/TAROT
|
705 |
Efficient Multivariate Robust Mean Estimation Under Mean-Shift Contamination
|
https://openreview.net/forum?id=LmT1of1LUJ
|
[
"Ilias Diakonikolas",
"Giannis Iakovidis",
"Daniel Kane",
"Thanasis Pittas"
] |
Poster
|
We study the algorithmic problem of robust mean estimation of an identity covariance Gaussian in the presence of mean-shift contamination. In this contamination model, we are given a set of points in $\mathbb{R}^d$ generated i.i.d. via the following process. For a parameter $\alpha<1/2$, the $i$-th sample $x_i$ is obtained as follows: with probability $1-\alpha$, $x_i$ is drawn from $\mathcal{N}(\mu, I)$, where $\mu \in \mathbb{R}^d$ is the target mean; and with probability $\alpha$, $x_i$ is drawn from $\mathcal{N}(z_i, I)$, where $z_i$ is unknown and potentially arbitrary. Prior work characterized the information-theoretic limits of this task. Specifically, it was shown that— in contrast to Huber contamination— in the presence of mean-shift contamination consistent estimation is possible. On the other hand, all known robust estimators in the mean-shift model have running times exponential in the dimension. Here we give the first computationally efficient algorithm for high-dimensional robust mean estimation with mean-shift contamination that can tolerate a constant fraction of outliers. In particular, our algorithm has near-optimal sample complexity, runs in sample-polynomial time, and approximates the target mean to any desired accuracy. Conceptually, our result contributes to a growing body of work that studies inference with respect to natural noise models lying in between fully adversarial and random settings.
|
mean estimation, high-dimensional inference, robust statistics, contamination, computational efficiency
|
We present the first polynomial-time algorithm that achieves optimal error for high-dimensional mean estimation in the mean-shift contamination model.
| 12,278 |
2502.14772
|
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] | null |
706 |
Accelerating Spectral Clustering under Fairness Constraints
|
https://openreview.net/forum?id=JgUBM5hwcM
|
[
"Francesco Tonin",
"Alex Lambert",
"Johan Suykens",
"Volkan Cevher"
] |
Poster
|
Fairness of decision-making algorithms is an increasingly important issue. In this paper, we focus on spectral clustering with group fairness constraints, where every demographic group is represented in each cluster proportionally as in the general population. We present a new efficient method for fair spectral clustering (Fair SC) by casting the Fair SC problem within the difference of convex functions (DC) framework. To this end, we introduce a novel variable augmentation strategy and employ an alternating direction method of multipliers type of algorithm adapted to DC problems. We show that each associated subproblem can be solved efficiently, resulting in higher computational efficiency compared to prior work, which required a computationally expensive eigendecomposition. Numerical experiments
demonstrate the effectiveness of our approach on both synthetic and real-world benchmarks, showing significant speedups in computation time over prior art, especially as the problem size grows. This work thus represents a considerable step forward towards the adoption of fair clustering in real-world applications.
|
fairness, spectral clustering, difference of convex, scalability
|
We improve the computation time for fair spectral clustering thanks to a new difference-of-convex formulation.
| 12,277 |
2506.08143
|
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] | null |
707 |
Overestimation in LLM Evaluation: A Controlled Large-Scale Study on Data Contamination’s Impact on Machine Translation
|
https://openreview.net/forum?id=MpjtvkvXDo
|
[
"Muhammed Yusuf Kocyigit",
"Eleftheria Briakou",
"Daniel Deutsch",
"Jiaming Luo",
"Colin Cherry",
"Markus Freitag"
] |
Poster
|
Data contamination—the accidental consumption of evaluation examples within the pre-training data—can undermine the validity of evaluation benchmarks. In this paper, we present a rigorous analysis of the effects of contamination on language models at 1B and 8B scales on the machine translation task. Starting from a carefully decontaminated train-test split, we systematically introduce contamination at various stages, scales, and data formats to isolate its effect and measure its impact on performance metrics. Our experiments reveal that contamination with both source and target substantially inflates BLEU scores, and this inflation is 2.5 times larger (up to 30 BLEU points) for 8B compared to 1B models. In contrast, source-only and target-only contamination generally produce smaller, less consistent over-estimations. Finally, we study how the temporal distribution and frequency of contaminated samples influence performance over-estimation across languages with varying degrees of data resources.
|
Data contamination, large language models(LLMs), machine translation, LLM evaluation, performance overestimation
|
We train ~40 models for each 1B and 8B models on different contamination settings and compare their performance with a clean baseline on the machine translation task and quantify the impact of different types of contamination.
| 12,266 | null |
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] | null |
708 |
PEAKS: Selecting Key Training Examples Incrementally via Prediction Error Anchored by Kernel Similarity
|
https://openreview.net/forum?id=G0My9EEJbw
|
[
"Mustafa Burak Gurbuz",
"Xingyu Zheng",
"Constantine Dovrolis"
] |
Poster
|
As deep learning continues to be driven by ever-larger datasets, understanding which examples are most important for generalization has become a critical question. While progress in data selection continues, emerging applications require studying this problem in dynamic contexts. To bridge this gap, we pose the Incremental Data Selection (IDS) problem, where examples arrive as a continuous stream, and need to be selected without access to the full data source. In this setting, the learner must incrementally build a training dataset of predefined size while simultaneously learning the underlying task. We find that in IDS, the impact of a new sample on the model state depends fundamentally on both its geometric relationship in the feature space and its prediction error. Leveraging this insight, we propose PEAKS (Prediction Error Anchored by Kernel Similarity), an efficient data selection method tailored for IDS. Our comprehensive evaluations demonstrate that PEAKS consistently outperforms existing selection strategies. Furthermore, PEAKS yields increasingly better performance returns than random selection as training data size grows on real-world datasets. The code is available at https://github.com/BurakGurbuz97/PEAKS.
|
data pruning, coresets, data selection, deep learning
|
We pose the Incremental Data Selection problem, where examples arrive as a continuous stream, and we propose PEAKS, a principled and efficient method tailored for this problem.
| 12,259 |
2504.05250
|
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] |
https://github.com/BurakGurbuz97/PEAKS
|
709 |
Geometric and Physical Constraints Synergistically Enhance Neural PDE Surrogates
|
https://openreview.net/forum?id=6f1Hx1eneF
|
[
"Yunfei Huang",
"David S. Greenberg"
] |
Poster
|
Neural PDE surrogates can improve the cost-accuracy tradeoff of classical solvers, but often generalize poorly to new initial conditions and accumulate errors over time. Physical and symmetry constraints have shown promise in closing this performance gap, but existing techniques for imposing these inductive biases are incompatible with the staggered grids commonly used in computational fluid dynamics. Here we introduce novel input and output layers that respect physical laws and symmetries on the staggered grids, and for the first time systematically investigate how these constraints, individually and in combination, affect the accuracy of PDE surrogates. We focus on two challenging problems: shallow water equations with closed boundaries and decaying incompressible turbulence. Compared to strong baselines, symmetries and physical constraints consistently improve performance across tasks, architectures, autoregressive prediction steps, accuracy measures, and network sizes. Symmetries are more effective than physical constraints, but surrogates with both performed best, even compared to baselines with data augmentation or pushforward training, while themselves benefiting from the pushforward trick. Doubly-constrained surrogates also generalize better to initial conditions and durations beyond the range of the training data, and more accurately predict real-world ocean currents.
|
Geometric and Physical Constraints, Neural PDE Surrogates, Symmetry Equivariance, Conservation Laws, Fluid Dynamics
| null | 12,257 |
2506.05513
|
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] | null |
710 |
Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design
|
https://openreview.net/forum?id=9qzpNSTUYp
|
[
"Masatoshi Uehara",
"Xingyu Su",
"Yulai Zhao",
"Xiner Li",
"Aviv Regev",
"Shuiwang Ji",
"Sergey Levine",
"Tommaso Biancalani"
] |
Poster
|
To fully leverage the capabilities of diffusion models, we are often interested in optimizing downstream reward functions during inference. While numerous algorithms for reward-guided generation have been recently proposed due to their significance, current approaches predominantly focus on single-shot generation, transitioning from fully noised to denoised states. We propose a novel framework for inference-time reward optimization with diffusion models. Our approach employs an iterative refinement process consisting of two steps in each iteration: noising and reward-guided denoising. This sequential refinement allows for the gradual correction of errors introduced during reward optimization. Finally, we provide a theoretical guarantee for our framework. Finally, we demonstrate its superior empirical performance in protein and DNA design.
|
Diffusion models
|
Diffusion models
| 12,256 |
2502.14944
|
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] |
https://github.com/masa-ue/ProDifEvo-Refinement
|
711 |
Pareto-Optimality, Smoothness, and Stochasticity in Learning-Augmented One-Max-Search
|
https://openreview.net/forum?id=ZTOVlPC5Vf
|
[
"Ziyad Benomar",
"Lorenzo Croissant",
"Vianney Perchet",
"Spyros Angelopoulos"
] |
Poster
|
One-max search is a classic problem in online decision-making, in which a trader acts on a sequence of revealed prices and accepts one of them irrevocably to maximise its profit. The problem has been studied both in probabilistic and in worst-case settings, notably through competitive analysis, and more recently in learning-augmented settings in which the trader has access to a prediction on the sequence. However, existing approaches either lack smoothness, or do not achieve optimal worst-case guarantees: they do not attain the best possible trade-off between the consistency and the robustness of the algorithm. We close this gap by presenting the first algorithm that simultaneously achieves both of these important objectives. Furthermore, we show how to leverage the obtained smoothness to provide an analysis of one-max search in stochastic learning-augmented settings which capture randomness in both the observed prices and the prediction.
|
Learning-augmented algorithms, algorithms with predictions, online algorithms, competitive ratio, one-max search
| null | 12,244 |
2502.05720
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] | null |
712 |
Looking Beyond the Top-1: Transformers Determine Top Tokens in Order
|
https://openreview.net/forum?id=2B11W1Z6ID
|
[
"Daria Lioubashevski",
"Tomer M. Schlank",
"Gabriel Stanovsky",
"Ariel Goldstein"
] |
Poster
|
Uncovering the inner mechanisms of Transformer models offers insights into how they process and represent information. In this work, we analyze the computation performed by Transformers in the layers after the top-1 prediction remains fixed, known as the “saturation event”. We expand this concept to top-k tokens, demonstrating that similar saturation events occur across language, vision, and speech models. We find that these events occur in order of the corresponding tokens’ ranking, i.e., the model first decides on the top ranking token, then the second highest ranking token, and so on. This phenomenon seems intrinsic to the Transformer architecture, occurring across different variants, and even in untrained Transformers. We propose that these events reflect task transitions, where determining each token corresponds to a discrete task. We show that it is possible to predict the current task from hidden layer embedding, and demonstrate that we can cause the model to switch to the next task via intervention. Leveraging our findings, we introduce a token-level early-exit strategy, surpassing existing methods in balancing performance and efficiency and show how to exploit saturation events for better language modeling.
|
mechanistic interpretability, transformer, large language model, efficient inference
| null | 12,238 |
2410.20210
|
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] |
https://github.com/daria-lioubashevski/beyond_top1
|
713 |
DriveGPT: Scaling Autoregressive Behavior Models for Driving
|
https://openreview.net/forum?id=SBUxQakoJJ
|
[
"Xin Huang",
"Eric M Wolff",
"Paul Vernaza",
"Tung Phan-Minh",
"Hongge Chen",
"David S Hayden",
"Mark Edmonds",
"Brian Pierce",
"Xinxin Chen",
"Pratik Elias Jacob",
"Xiaobai Chen",
"Chingiz Tairbekov",
"Pratik Agarwal",
"Tianshi Gao",
"Yuning Chai",
"Siddhartha Srinivasa"
] |
Poster
|
We present DriveGPT, a scalable behavior model for autonomous driving. We model driving as a sequential decision-making task, and learn a transformer model to predict future agent states as tokens in an autoregressive fashion. We scale up our model parameters and training data by multiple orders of magnitude, enabling us to explore the scaling properties in terms of dataset size, model parameters, and compute. We evaluate DriveGPT across different scales in a planning task, through both quantitative metrics and qualitative examples, including closed-loop driving in complex real-world scenarios. In a separate prediction task, DriveGPT outperforms state-of-the-art baselines and exhibits improved performance by pretraining on a large-scale dataset, further validating the benefits of data scaling.
|
Autonomous Driving, Foundation Models, Behavior Modeling
|
Our paper studies the scaling properties in driving, across multiple orders of magnitude in data, model size, and compute.
| 12,237 |
2412.14415
|
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] | null |
714 |
Multiaccuracy and Multicalibration via Proxy Groups
|
https://openreview.net/forum?id=sGny74zx2V
|
[
"Beepul Bharti",
"Mary Versa Clemens-Sewall",
"Paul Yi",
"Jeremias Sulam"
] |
Poster
|
As the use of predictive machine learning algorithms increases in high-stakes decision-making, it is imperative that these algorithms are fair across sensitive groups. However, measuring and enforcing fairness in real-world applications can be challenging due to missing or incomplete sensitive group information. Proxy-sensitive attributes have been proposed as a practical and effective solution in these settings, but only for parity-based fairness notions. Knowing how to evaluate and control for fairness with missing sensitive group data for newer, different, and more flexible frameworks, such as multiaccuracy and multicalibration, remains unexplored. In this work, we address this gap by demonstrating that in the absence of sensitive group data, proxy-sensitive attributes can provably be used to derive actionable upper bounds on the true multiaccuracy and multicalibration violations, providing insights into a predictive model’s potential worst-case fairness violations. Additionally, we show that adjusting models to satisfy multiaccuracy and multicalibration across proxy-sensitive attributes can significantly mitigate these violations for the true, but unknown, sensitive groups. Through several experiments on real-world datasets, we illustrate that approximate multiaccuracy and multicalibration can be achieved even when sensitive group data is incomplete or unavailable.
|
multicalibration, multiaccuracy, fairness
|
This work demonstrates how to evaluate and control for multiaccuracy and multicalibration when sensitive group data is missing by using proxies.
| 12,229 |
2503.02870
|
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0.008903197944164276,
-0.007065712008625269,
-0.003501090221107006
] |
https://github.com/Sulam-Group/proxy_ma-mc
|
715 |
ADIOS: Antibody Development via Opponent Shaping
|
https://openreview.net/forum?id=KVt0TeQ5Ne
|
[
"Sebastian Rene Towers",
"Aleksandra Kalisz",
"Philippe A. Robert",
"Alicia Higueruelo",
"Francesca Vianello",
"Chloe Ming-Han Tsai",
"Harrison Steel",
"Jakob Nicolaus Foerster"
] |
Poster
|
Anti-viral therapies are typically designed to target only the current strains of a virus, a *myopic* response. However, therapy-induced selective pressures drive the emergence of new viral strains, against which the original myopic therapies are no longer effective. This evolutionary response presents an opportunity: our therapies could both *defend against and actively influence viral evolution*. This motivates our method ADIOS: Antibody Development vIa Opponent Shaping. ADIOS is a meta-learning framework where the process of antibody therapy design, the *outer loop*, accounts for the virus's adaptive response, the *inner loop*. With ADIOS, antibodies are not only robust against potential future variants, they also influence, i.e. *shape*, which future variants emerge. In line with the opponent shaping literature, we refer to our optimised antibodies as *shapers*. To demonstrate the value of ADIOS, we build a viral evolution simulator using the Absolut! framework, in which shapers successfully target both current and future viral variants, outperforming myopic antibodies. Furthermore, we show that shapers modify the distribution over viral evolutionary trajectories to result in weaker variants.
We believe that our ADIOS paradigm will facilitate the discovery of long-lived vaccines and antibody therapies while also generalising to other domains. Specifically, domains such as antimicrobial resistance, cancer treatment, and others with evolutionarily adaptive opponents. Our code is available at https://github.com/olakalisz/adios.
|
Opponent Shaping, Antibody Design, Meta Learning, Game Theory, Computational Biology
|
We present ADIOS: a method for designing antibody "shapers" which both act as effective antiviral therapies and influence the viral evolution towards less harmful variants.
| 12,226 |
2409.10588
|
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] |
https://github.com/olakalisz/adios
|
716 |
Universal Approximation of Mean-Field Models via Transformers
|
https://openreview.net/forum?id=vs1u8WKlB5
|
[
"Shiba Biswal",
"Karthik Elamvazhuthi",
"Rishi Sonthalia"
] |
Poster
|
This paper investigates the use of transformers to approximate the mean-field dynamics of interacting particle systems exhibiting collective behavior. Such systems are fundamental in modeling phenomena across physics, biology, and engineering, including opinion formation, biological networks, and swarm robotics. The key characteristic of these systems is that the particles are indistinguishable, leading to permutation-equivariant dynamics. First, we empirically demonstrate that transformers are well-suited for approximating a variety of mean field models, including the Cucker-Smale model for flocking and milling, and the mean-field system for training two-layer neural networks. We validate our numerical experiments via mathematical theory. Specifically, we prove that if a finite-dimensional transformer effectively approximates the finite-dimensional vector field governing the particle system, then the $L_\infty$ distance between the \textit{expected transformer} and the infinite-dimensional mean-field vector field can be bounded by a function of the number of particles observed during training. Leveraging this result, we establish theoretical bounds on the distance between the true mean-field dynamics and those obtained using the transformer.
|
Mean Field Equations, Transformers, Universal Approximation, Collective Behavior
|
We empirically and theoretically show that transformers can approximate infinite dimensional mean field equations
| 12,224 |
2410.16295
|
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] |
https://github.com/rsonthal/Mean-Field-Transformers
|
717 |
What Makes a Good Feedforward Computational Graph?
|
https://openreview.net/forum?id=w9HPYVpfvY
|
[
"Alex Vitvitskyi",
"João Guilherme Madeira Araújo",
"Marc Lackenby",
"Petar Veličković"
] |
Poster
|
As implied by the plethora of literature on graph rewiring, the choice of computational graph employed by a neural network can make a significant impact on its downstream performance. Certain effects related to the computational graph, such as under-reaching and over-squashing, may even render the model incapable of learning certain functions. Most of these effects have only been thoroughly studied in the domain of undirected graphs; however, recent years have seen a significant rise in interest in feedforward computational graphs: directed graphs without any back edges. In this paper, we study the desirable properties of a feedforward computational graph, discovering two important complementary measures: fidelity and mixing time, and evaluating a few popular choices of graphs through the lens of these measures. Our study is backed by both theoretical analyses of the metrics' asymptotic behaviour for various graphs, as well as correlating these metrics to the performance of trained neural network models using the corresponding graphs.
|
graphs, graph neural networks, random walks, mixing time
|
We study the desirable traits for a feedforward computational graph used by a neural network, finding fidelity and mixing time to be important complementary metrics to optimise.
| 12,222 |
2502.06751
|
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] | null |
718 |
NeuronTune: Towards Self-Guided Spurious Bias Mitigation
|
https://openreview.net/forum?id=qC5FZs34Xr
|
[
"Guangtao Zheng",
"Wenqian Ye",
"Aidong Zhang"
] |
Poster
|
Deep neural networks often develop spurious bias, reliance on correlations between non-essential features and classes for predictions. For example, a model may identify objects based on frequently co-occurring backgrounds rather than intrinsic features, resulting in degraded performance on data lacking these correlations. Existing mitigation approaches typically depend on external annotations of spurious correlations, which may be difficult to obtain and are not relevant to the spurious bias in a model. In this paper, we take a step towards self-guided mitigation of spurious bias by proposing NeuronTune, a post hoc method that directly intervenes in a model's internal decision process. Our method probes in a model's latent embedding space to identify and regulate neurons that lead to spurious prediction behaviors. We theoretically justify our approach and show that it brings the model closer to an unbiased one. Unlike previous methods, NeuronTune operates without requiring spurious correlation annotations, making it a practical and effective tool for improving model robustness. Experiments across different architectures and data modalities demonstrate that our method significantly mitigates spurious bias in a self-guided way.
|
spurious correlation, robustness, bias mitigation
|
We introduce NeuronTune, which mitigates spurious bias in neural networks by adjusting biased neurons without needing external annotations.
| 12,215 |
2505.24048
|
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] |
https://github.com/gtzheng/NeuronTune
|
719 |
Improving Rationality in the Reasoning Process of Language Models through Self-playing Game
|
https://openreview.net/forum?id=PPsiS5nSlv
|
[
"Pinzheng Wang",
"Juntao Li",
"Zecheng Tang",
"Haijia Gui",
"Min zhang"
] |
Poster
|
Large language models (LLMs) have demonstrated considerable reasoning abilities in various tasks such as mathematics and coding.
However, recent studies indicate that even the best models lack true comprehension of their reasoning processes.
In this paper, we explore how self-play can enhance the rationality of models in the reasoning process without supervision from humans or superior models.
We design a $\textit{\textbf{C}ritic-\textbf{D}iscernment \textbf{G}ame}~(\textbf{CDG})$ in which a prover first provides a solution to a given problem and is subsequently challenged by critiques of its solution.
These critiques either aim to assist or mislead the prover.
The objective of the prover is to maintain the correct answer when faced with misleading comments, while correcting errors in response to constructive feedback.
Our experiments on tasks involving mathematical reasoning, stepwise error detection, self-correction, and long-chain reasoning demonstrate that CDG training can significantly improve the ability of well-aligned LLMs to comprehend their reasoning process.
|
Self-play, Large Language Models, LLM Reasoning
|
We explore how self-play can enhance the rationality of LLMs in the reasoning process.
| 12,211 |
2506.22920
|
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] |
https://github.com/PinzhengWang322/Critic_Discernment_Game
|
720 |
Model-Based Exploration in Monitored Markov Decision Processes
|
https://openreview.net/forum?id=GdsbEOwAE7
|
[
"Alireza Kazemipour",
"Matthew E. Taylor",
"Michael Bowling"
] |
Poster
|
A tenet of reinforcement learning is that the agent always observes rewards. However, this is not true in many realistic settings, e.g., a human observer may not always be available to provide rewards, sensors may be limited or malfunctioning, or rewards may be inaccessible during deployment. Monitored Markov decision processes (Mon-MDPs) have recently been proposed to model such settings. However, existing Mon-MDP algorithms have several limitations: they do not fully exploit the problem structure, cannot leverage a known monitor, lack worst-case guarantees for "unsolvable" Mon-MDPs without specific initialization, and offer only asymptotic convergence proofs. This paper makes three contributions. First, we introduce a model-based algorithm for Mon-MDPs that addresses these shortcomings. The algorithm employs two instances of model-based interval estimation: one to ensure that observable rewards are reliably captured, and another to learn the minimax-optimal policy. Second, we empirically demonstrate the advantages. We show faster convergence than prior algorithms in more than four dozen benchmarks, and even more dramatic improvements when the monitoring process is known. Third, we present the first finite-sample bound on performance. We show convergence to a minimax-optimal policy even when some rewards are never observable.
|
Exploration-Exploitation, Model-Based Interval Estimation, Monitored Markov Decision Processes
|
This paper introduces a model-based algorithm for sequential decision-making in the face of partially observable rewards.
| 12,205 |
2502.16772
|
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] |
https://github.com/IRLL/Exploration-in-Mon-MDPs
|
721 |
LASER: Attention with Exponential Transformation
|
https://openreview.net/forum?id=idPoj6ZeDs
|
[
"Sai Surya Duvvuri",
"Inderjit S Dhillon"
] |
Poster
|
Transformers have had tremendous impact for several sequence related tasks, largely due to their ability to retrieve from any part of the sequence via softmax based dot-product attention. This mechanism plays a crucial role in Transformer's performance. We analyze the gradients backpropagated through the softmax operation in the attention mechanism and observe that these gradients can often be small. This poor gradient signal backpropagation can lead to inefficient learning of parameters preceeding the attention operations. To this end, we introduce a new attention mechanism called LASER, which we analytically show to admit a larger gradient signal. We show that LASER attention can be implemented by making small modifications to existing attention implementations. We conduct experiments on autoregressive large language models (LLMs) with upto 7.7 billion parameters with an average improvement of upto 1.44% over standard attention on downstream evaluations and 1.65% finetuning improvements. Additionally, LASER demonstrates generalization performance improvement across a variety of tasks (vision, text and speech):Vision Transformer (ViT) on Imagenet, Conformer on the Librispeech speech-to-text and BERT with 2.2 billion parameters.
|
Large language modeling, deep learning, transformer, Conformer, ViT
|
We identify an issue with gradients backpropagating through standard attention mechanism in large language model and propose LASER to alleviate this issue.
| 12,201 |
2411.03493
|
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] | null |
722 |
Collapse-Proof Non-Contrastive Self-Supervised Learning
|
https://openreview.net/forum?id=wIfl8PK6Op
|
[
"Emanuele Sansone",
"Tim Lebailly",
"Tinne Tuytelaars"
] |
Poster
|
We present a principled and simplified design of the projector and loss function for non-contrastive self-supervised learning based on hyperdimensional computing. We theoretically demonstrate that this design introduces an inductive bias that encourages representations to be simultaneously decorrelated and clustered, without explicitly enforcing these properties. This bias provably enhances generalization and suffices to avoid known training failure modes, such as representation, dimensional, cluster, and intracluster collapses. We validate our theoretical findings on image datasets, including SVHN, CIFAR-10, CIFAR-100, and ImageNet-100. Our approach effectively combines the strengths of feature decorrelation and cluster-based self-supervised learning methods, overcoming training failure modes while achieving strong generalization in clustering and linear classification tasks.
|
self-supervised learning, hyperdimensional computing, feature decorrelation, clustering, collapses
|
Principled and simplified design of the projector and loss function for non-contrastive self-supervised learning based on hyperdimensional computing
| 12,199 |
2410.04959
|
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] |
https://github.com/emsansone/CPLearn
|
723 |
Censor Dependent Variational Inference
|
https://openreview.net/forum?id=fHD76uOB4t
|
[
"Chuanhui Liu",
"Xiao Wang"
] |
Poster
|
This paper provides a comprehensive analysis of variational inference in latent variable models for survival analysis, emphasizing the distinctive challenges associated with applying variational methods to survival data. We identify a critical weakness in the existing methodology, demonstrating how a poorly designed variational distribution may hinder the objective of survival analysis tasks—modeling time-to-event distributions. We prove that the optimal variational distribution, which perfectly bounds the log-likelihood, may depend on the censoring mechanism. To address this issue, we propose censor-dependent variational inference (CDVI), tailored for latent variable models in survival analysis. More practically, we introduce CD-CVAE, a V-structure Variational Autoencoder (VAE) designed for the scalable implementation of CDVI. Further discussion extends some existing theories and training techniques to survival analysis. Extensive experiments validate our analysis and demonstrate significant improvements in the estimation of individual survival distributions.
|
survival analysis, variational inference, variational autoencoders, time-to-event modeling
|
Variational inference for survival models transcends the standard paradim, requiring adaptions to handle censored data.
| 12,192 |
2502.09591
|
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] |
https://github.com/ChuanhuiLiu/CDVI
|
724 |
Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search
|
https://openreview.net/forum?id=j4FXxMiDjL
|
[
"Maohao Shen",
"Guangtao Zeng",
"Zhenting Qi",
"Zhang-Wei Hong",
"Zhenfang Chen",
"Wei Lu",
"Gregory W. Wornell",
"Subhro Das",
"David Daniel Cox",
"Chuang Gan"
] |
Poster
|
Large language models (LLMs) have demonstrated remarkable reasoning capabilities across diverse domains. Recent studies have shown that increasing test-time computation enhances LLMs' reasoning capabilities. This typically involves extensive sampling at inference time guided by an external LLM verifier, resulting in a two-player system. Despite external guidance, the effectiveness of this system demonstrates the potential of a single LLM to tackle complex tasks. Thus, we pose a new research problem: *Can we internalize the searching capabilities to fundamentally enhance the reasoning abilities of a single LLM?* This work explores an orthogonal direction focusing on post-training LLMs for autoregressive searching (*i.e.,* an extended reasoning process with self-reflection and self-exploration of new strategies). To achieve this, we propose the Chain-of-Action-Thought (COAT) reasoning and a two-stage training paradigm: 1) a small-scale format tuning stage to internalize the COAT reasoning format and 2) a large-scale self-improvement stage leveraging reinforcement learning. Our approach results in Satori, a 7B LLM trained on open-source models and data. Extensive empirical evaluations demonstrate that Satori achieves state-of-the-art performance on mathematical reasoning benchmarks while exhibits strong generalization to out-of-domain tasks. Code, data, and models are fully open-sourced.
|
reasoning, self-improvement, large language model, post-training, reinforcement learning
| null | 12,174 |
2502.02508
|
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] |
https://github.com/satori-reasoning/Satori
|
725 |
Deep Neural Cellular Potts Models
|
https://openreview.net/forum?id=3xznpzabYQ
|
[
"Koen Minartz",
"Tim D'Hondt",
"Leon Hillmann",
"Jörn Starruß",
"Lutz Brusch",
"Vlado Menkovski"
] |
Poster
|
The cellular Potts model (CPM) is a powerful computational method for simulating collective spatiotemporal dynamics of biological cells.
To drive the dynamics, CPMs rely on physics-inspired Hamiltonians. However, as first principles remain elusive in biology, these Hamiltonians only approximate the full complexity of real multicellular systems.
To address this limitation, we propose NeuralCPM, a more expressive cellular Potts model that can be trained directly on observational data.
At the core of NeuralCPM lies the Neural Hamiltonian, a neural network architecture that respects universal symmetries in collective cellular dynamics.
Moreover, this approach enables seamless integration of domain knowledge by combining known biological mechanisms and the expressive Neural Hamiltonian into a hybrid model.
Our evaluation with synthetic and real-world multicellular systems demonstrates that NeuralCPM is able to model cellular dynamics that cannot be accounted for by traditional analytical Hamiltonians.
|
Cellular Potts Models, Multi-Cell Dynamics, Cell Migration, Energy-Based Models, Simulation
|
We introduce Neural cellular Potts models, a method for data-driven simulation of multicellular systems.
| 12,169 |
2502.02129
|
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] |
https://github.com/kminartz/NeuralCPM
|
726 |
Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options
|
https://openreview.net/forum?id=EiSXjzgBK4
|
[
"Lakshmi Nair",
"Ian Trase",
"J. Mark Kim"
] |
Poster
|
We present a novel reasoning approach called Flow-of-Options (FoO), designed to address intrinsic biases in Large Language Models (LLMs). Flow-of-Options enables LLMs to systematically explore a diverse range of possibilities in their reasoning, as demonstrated by an FoO-based agentic framework developed for autonomously solving Machine Learning (ML) tasks. FoO enforces diversity in LLM solutions through compressed and interpretable task representations, resulting in improvements of 38.2% - 69.2% on standard data science tasks, and 37.4% - 47.9% on therapeutic chemistry tasks, as compared to state-of-the-art baselines. With an overall operation cost under $1 per task, our framework is well-suited for cost-sensitive applications. Going beyond tabular classification and regression, we show the broader applicability of our FoO-based agentic system to tasks such as reinforcement learning and image generation. Our code is open-sourced at: https://github.com/flagshippioneering/Flow-of-Options.
|
LLM Agents, AutoML, LLM reasoning
| null | 12,168 | null |
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] |
https://github.com/flagshippioneering/Flow-of-Options
|
727 |
Guided Search Strategies in Non-Serializable Environments with Applications to Software Engineering Agents
|
https://openreview.net/forum?id=NMdWQXosFs
|
[
"Karina Zainullina",
"Alexander Golubev",
"Maria Trofimova",
"Sergei Polezhaev",
"Ibragim Badertdinov",
"Daria Litvintseva",
"Simon Karasik",
"Filipp Fisin",
"Sergei Skvortsov",
"Maksim Nekrashevich",
"Anton Shevtsov",
"Boris Yangel"
] |
Poster
|
Large language models (LLMs) have recently achieved remarkable results in complex multi-step tasks, such as mathematical reasoning and agentic software engineering. However, they often struggle to maintain consistent performance across multiple solution attempts. One effective approach to narrow the gap between average-case and best-case performance is guided test-time search, which explores multiple solution paths to identify the most promising one. Unfortunately, effective search techniques (e.g. MCTS) are often unsuitable for *non-serializable* RL environments, such as Docker containers, where intermediate environment states cannot be easily saved and restored. We investigate two complementary search strategies applicable to such environments: 1-step lookahead and trajectory selection, both guided by a learned action-value function estimator. On the SWE-bench Verified benchmark, a key testbed for agentic software engineering, we find these methods to double the average success rate of a fine-tuned Qwen-72B model, achieving $40.8$\%, the new state-of-the-art for open-weights models. Additionally, we show that these techniques are transferable to more advanced closed models, yielding similar improvements with GPT-4o.
|
RL, Planning, Test-time search, Test-time computation, Agents, LLM, Software Engineering, SWE-bench, Process supervision, Outcome supervision
|
We apply test-time search with learned critics to LLM agents even in environments where it's not possible to fork a state by following multiple actions from it.
| 12,167 |
2505.13652
|
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] | null |
728 |
Federated Oriented Learning: A Practical One-Shot Personalized Federated Learning Framework
|
https://openreview.net/forum?id=jwjvkWsePB
|
[
"Guan Huang",
"Tao Shu"
] |
Poster
|
Personalized Federated Learning (PFL) has become a promising learning paradigm, enabling the training of high-quality personalized models through multiple communication rounds between clients and a central server. However, directly applying traditional PFL in real-world environments where communication is expensive, limited, or infeasible is challenging, as seen in Low Earth Orbit (LEO) satellite constellations, which face severe communication constraints due to their high mobility, limited contact windows. To address these issues, we introduce Federated Oriented Learning (FOL), a novel four-stage one-shot PFL algorithm designed to enhance local model performance by leveraging neighboring models within stringent communication constraints. FOL comprises model pretraining, model collection, model alignment (via fine-tuning, pruning, post fine-tuning, and ensemble refinement), and knowledge distillation stages. We establish two theoretical guarantees on empirical risk discrepancy between student and teacher models and the convergence of the distillation process. Extensive experiments on datasets Wildfire, Hurricane, CIFAR-10, CIFAR-100, and SVHN demonstrate that FOL consistently outperforms state-of-the-art one-shot Federated Learning (OFL) methods; for example, it achieves accuracy improvements of up to 39.24\% over the baselines on the Wildfire dataset.
|
Low Earth Orbit Satellites, Model Personalization, One-Shot Federated Learning, Communication Constraints
|
We introduce Federated Oriented Learning (FOL), a one-shot personalized federated learning framework that improves local models under communication constraints through model alignment, ensemble refinement, and knowledge distillation.
| 12,166 | null |
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] | null |
729 |
Feedforward Few-shot Species Range Estimation
|
https://openreview.net/forum?id=u08HAb5WyC
|
[
"Christian Lange",
"Max Hamilton",
"Elijah Cole",
"Alexander Shepard",
"Samuel Heinrich",
"Angela Zhu",
"Subhransu Maji",
"Grant Van Horn",
"Oisin Mac Aodha"
] |
Poster
|
Knowing where a particular species can or cannot be found on Earth is crucial for ecological research and conservation efforts.
By mapping the spatial ranges of all species, we would obtain deeper insights into how global biodiversity is affected by climate change and habitat loss. However, accurate range estimates are only available for a relatively small proportion of all known species. For the majority of the remaining species, we typically only have a small number of records denoting the spatial locations where they have previously been observed. We outline a new approach for few-shot species range estimation to address the challenge of accurately estimating the range of a species from limited data. During inference, our model takes a set of spatial locations as input, along with optional metadata such as text or an image, and outputs a species encoding that can be used to predict the range of a previously unseen species in a feedforward manner. We evaluate our approach on two challenging benchmarks, where we obtain state-of-the-art range estimation performance, in a fraction of the compute time, compared to recent alternative approaches.
|
species distribution modeling, SDM, species range modelling, spatial implicit neural representation, SINR, low-shot learning, few-shot learning
|
A new low-shot method for estimating the spatial range of species
| 12,159 |
2502.14977
|
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] |
https://github.com/Chris-lange/fs-sinr
|
730 |
Improving Multi-Class Calibration through Normalization-Aware Isotonic Techniques
|
https://openreview.net/forum?id=PuVmGAggkU
|
[
"Alon Arad",
"Saharon Rosset"
] |
Poster
|
Accurate and reliable probability predictions are essential for multi-class supervised learning tasks, where well-calibrated models enable rational decision-making. While isotonic regression has proven effective for binary calibration, its extension to multi-class problems via one-vs-rest calibration often produces suboptimal results, limiting its practical adoption. In this work, we propose novel isotonic normalization-aware techniques for multi-class calibration, grounded in natural and intuitive assumptions expected by practitioners. Unlike prior approaches, our methods inherently account for probability normalization by either incorporating normalization directly into the optimization process (**NA-FIR**) or modeling the problem as a cumulative bivariate isotonic regression (**SCIR**). Empirical evaluations on a variety of text and image classification datasets across different model architectures reveal that our approach consistently improves log loss and expected calibration error (ECE) metrics. These findings underscore the potential of our approach to enhance a-parametric multi-class calibration practices, offering an adaptable solution for real-world applications.
|
Machine Learning, Calibration, Multi-class, Isotonic Regression, Uncertainty Estimation
|
We propose novel isotonic normalization-aware post-hoc calibration techniques that inherently account for probability normalization, addressing a key limitation of prior approaches.
| 12,156 | null |
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] | null |
731 |
Decoding Rewards in Competitive Games: Inverse Game Theory with Entropy Regularization
|
https://openreview.net/forum?id=L47M0km5dq
|
[
"Junyi Liao",
"Zihan Zhu",
"Ethan X Fang",
"Zhuoran Yang",
"Vahid Tarokh"
] |
Poster
|
Estimating the unknown reward functions driving agents' behavior is a central challenge in inverse games and reinforcement learning. This paper introduces a unified framework for reward function recovery in two-player zero-sum matrix games and Markov games with entropy regularization. Given observed player strategies and actions, we aim to reconstruct the underlying reward functions. This task is challenging due to the inherent ambiguity of inverse problems, the non-uniqueness of feasible rewards, and limited observational data coverage. To address these challenges, we establish reward function identifiability using the quantal response equilibrium (QRE) under linear assumptions. Building on this theoretical foundation, we propose an algorithm to learn reward from observed actions, designed to capture all plausible reward parameters by constructing confidence sets. Our algorithm works in both static and dynamic settings and is adaptable to incorporate other methods, such as Maximum Likelihood Estimation (MLE). We provide strong theoretical guarantees for the reliability and sample-efficiency of our algorithm. Empirical results demonstrate the framework’s effectiveness in accurately recovering reward functions across various scenarios, offering new insights into decision-making in competitive environments.
|
Inverse game; Quantal Response Equilibrium; Reward recovery; confidence sets
|
his paper introduces a unified framework for reward function recovery in two-player zero-sum matrix games and Markov games with entropy regularization.
| 12,155 | null |
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] | null |
732 |
Testing the Limits of Fine-Tuning for Improving Visual Cognition in Vision Language Models
|
https://openreview.net/forum?id=jSxU7ZGe3B
|
[
"Luca M. Schulze Buschoff",
"Konstantinos Voudouris",
"Elif Akata",
"Matthias Bethge",
"Joshua B. Tenenbaum",
"Eric Schulz"
] |
Poster
|
Pre-trained vision language models still fall short of human visual cognition. In an effort to improve visual cognition and align models with human behavior, we introduce visual stimuli and human judgments on visual cognition tasks, allowing us to systematically evaluate performance across cognitive domains under a consistent environment. We fine-tune models on ground truth data for intuitive physics and causal reasoning and find that this improves model performance in the respective fine-tuning domain. Furthermore, it can improve model alignment with human behavior. However, we find that task-specific fine-tuning does not contribute to robust human-like generalization to data with other visual characteristics or to tasks in other cognitive domains.
|
Cognitive Science, Machine Learning, Fine-tuning, Vision language models, Intuitive phyics, Causal reasoning, Counterfactual reasoning
|
Fine-tuning can make models better at visual cognition tasks, but it does not lead to robust human-like generalization to other tasks.
| 12,153 |
2502.15678
|
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] | null |
733 |
Aligned Multi Objective Optimization
|
https://openreview.net/forum?id=OQXpFh0hqf
|
[
"Yonathan Efroni",
"Ben Kretzu",
"Daniel Jiang",
"Jalaj Bhandari",
"Zheqing Zhu",
"Karen Ullrich"
] |
Poster
|
To date, the multi-objective optimization literature has mainly focused on conflicting objectives, studying the Pareto front, or requiring users to balance tradeoffs. Yet, in machine learning practice, there are many scenarios where such conflict does not take place. Recent findings from multi-task learning, reinforcement learning, and LLMs training show that diverse related tasks can enhance performance across objectives simultaneously. Despite this evidence, such phenomenon has not been examined from an optimization perspective. This leads to a lack of generic gradient-based methods that can scale to scenarios with a large number of related objectives. To address this gap, we introduce the Aligned Multi-Objective Optimization framework, propose new algorithms for this setting, and provide theoretical guarantees of its superior performance compared to naive approaches.
|
Multi objective optimization, optimization, multi-task learning
|
We develop gradient descent algorithms for solving optimization problems with multiple aligned objectives and providing provable improved performance guarantees of our adaptations.
| 12,151 |
2502.14096
|
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-0.0764729231595993,
0.04475313052535057,
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0.005701137240976095,
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] |
https://github.com/facebookresearch/AlignedMultiObjectiveOptimization
|
734 |
Organize the Web: Constructing Domains Enhances Pre-Training Data Curation
|
https://openreview.net/forum?id=boSqwdvJVC
|
[
"Alexander Wettig",
"Kyle Lo",
"Sewon Min",
"Hannaneh Hajishirzi",
"Danqi Chen",
"Luca Soldaini"
] |
Poster
|
Modern language models are trained on large, unstructured datasets consisting of trillions of tokens and obtained by crawling the web. The unstructured nature makes it difficult to reason about their contents and develop systematic approaches to data curation. In this paper, we unpack monolithic web corpora by developing taxonomies of their contents and organizing them into domains. We introduce WebOrganizer, a framework for organizing web pages in terms of both their topic and format. Using these two complementary notions of domains, we automatically annotate pre-training data by distilling annotations from a large language model into efficient classifiers. This allows us to study how data from different domains should be mixed to improve models on downstream tasks, and we show that we can combine insights about effective topics and formats to further boost performance. We demonstrate that our domain mixing also improves existing methods that select data based on quality. Furthermore, we study and compare how quality-based methods will implicitly change the domain mixture. Overall, our work demonstrates that constructing and mixing domains provides a valuable complement to quality-based data curation methods, opening new avenues for effective and insightful pre-training data curation.
|
pre-training, data-centric, data curation, language models
|
We construct novel domains for unstructured web data and demonstrate how it leads to better data curation
| 12,137 |
2502.10341
|
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] |
https://github.com/CodeCreator/WebOrganizer
|
735 |
Harnessing Heterogeneous Statistical Strength for Personalized Federated Learning via Hierarchical Bayesian Inference
|
https://openreview.net/forum?id=Zn6hmmBnAa
|
[
"Mahendra Singh Thapa",
"Rui Li"
] |
Poster
|
Personalized federated learning (PFL) based on Bayesian approach tackle the challenges from statistical heterogeneity of client data by computing a personalized posterior distribution over the parameters of each client's local model and constructing a global distribution by aggregating the parameters of these personalized posteriors. However, the heuristic aggregation methods introduce strong biases and result in global models with poor generalization. We thus propose a novel hierarchical Bayesian inference framework for PFL by specifying a conjugate hyper-prior over the parameters of the personalized posteriors. This allows us to jointly compute a global posterior distribution for aggregation and the personalized ones at local level. This hierarchical Bayesian inference framework achieves elegant balance between local personalization and global model robustness. Extensive empirical study shows that by effectively sharing the heterogeneous statistical strength across the local models while retaining their distinctive characteristics, our framework yields state-of-the-art performance. We also show that existing Bayesian PFLs are special cases of our framework.
|
Federated Learning, Bayesian Inference, Variational Inference
| null | 12,131 | null |
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] |
https://github.com/mahendrathapa/pFedHB
|
736 |
Field Matching: an Electrostatic Paradigm to Generate and Transfer Data
|
https://openreview.net/forum?id=9dHilxylvC
|
[
"Alexander Kolesov",
"S. I. Manukhov",
"Vladimir Vladimirovich Palyulin",
"Alexander Korotin"
] |
Poster
|
We propose Electrostatic Field Matching (EFM), a novel method that is suitable for both generative modelling and distribution transfer tasks. Our approach is inspired by the physics of an electrical capacitor. We place source and target distributions on the capacitor plates and assign them positive and negative charges, respectively. We then learn the capacitor's electrostatic field using a neural network approximator. To map the distributions to each other, we start at one plate of the capacitor and move the samples along the learned electrostatic field lines until they reach the other plate. We theoretically justify that this approach provably yields the distribution transfer. In practice, we demonstrate the performance of our EFM in toy and image data experiments.
|
generative models, distribution transfer, electrostatics
|
The New physics-inspired paradigm for Generative modeling
| 12,128 |
2502.02367
|
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] |
https://github.com/justkolesov/FieldMatching
|
737 |
R*: Efficient Reward Design via Reward Structure Evolution and Parameter Alignment Optimization with Large Language Models
|
https://openreview.net/forum?id=qZMLrURRr9
|
[
"Pengyi Li",
"Jianye HAO",
"Hongyao Tang",
"Yifu Yuan",
"Jinbin Qiao",
"Zibin Dong",
"YAN ZHENG"
] |
Poster
|
Reward functions are crucial for policy learning. Large Language Models (LLMs), with strong coding capabilities and valuable domain knowledge, provide an automated solution for high-quality reward design.
However,
code-based reward functions require precise guiding logic and parameter configurations within a vast design space, leading to low optimization efficiency.
To address the challenges,
we propose an efficient automated reward design framework, called R*,
which decomposes reward design into two parts: reward structure evolution and parameter alignment optimization. To design high-quality reward structures, R* maintains a reward function population and modularizes the functional components. LLMs are employed as the mutation operator, and module-level crossover is proposed to facilitate efficient exploration and exploitation.
To design more efficient reward parameters, R* first leverages LLMs to generate multiple critic functions for trajectory comparison and annotation. Based on these critics, a voting mechanism is employed to collect the trajectory segments with high-confidence labels.
These labeled segments are then used to refine the reward function parameters through preference learning.
Experiments on diverse robotic control tasks demonstrate that R* outperforms strong baselines in both reward design efficiency and quality, surpassing human-designed reward functions.
|
Reward Design, Reinforcement Learning
| null | 12,111 | null |
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] | null |
738 |
Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging
|
https://openreview.net/forum?id=MFNIka7nx0
|
[
"Pierre Ablin",
"Angelos Katharopoulos",
"Skyler Seto",
"David Grangier"
] |
Poster
|
Machine learning models are routinely trained on a mixture of different data domains.
Different domain weights yield very different downstream performances.
We propose the Soup-of-Experts, a novel architecture that can instantiate a model at test time for any domain weights with minimal computational cost and without re-training the model.
Our architecture consists of a bank of expert parameters, which are linearly combined to instantiate one model.
We learn the linear combination coefficients as a function of the input domain weights.
To train this architecture, we sample random domain weights, instantiate the corresponding model, and backprop through one batch of data sampled with these domain weights.
We demonstrate how our approach obtains small specialized models on several language modeling tasks quickly.
Soup-of-Experts are particularly appealing when one needs to ship many different specialist models quickly under a size constraint.
|
Pre-training, Specialization, Domain Adaptation, small models
|
We propose a new architecture that, given a specialist dataset, is able to quickly instantiate a small specialist model that is good on that dataset.
| 12,107 | null |
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] | null |
739 |
LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws
|
https://openreview.net/forum?id=IVUjRWnU6c
|
[
"Prasanna Mayilvahanan",
"Thaddäus Wiedemer",
"Sayak Mallick",
"Matthias Bethge",
"Wieland Brendel"
] |
Poster
|
Scaling laws guide the development of large language models (LLMs) by offering estimates for the optimal balance of model size, tokens, and compute.
More recently, loss-to-loss scaling laws that relate losses across pretraining datasets and downstream tasks have emerged as a powerful tool for understanding and improving LLM performance and generalization.
In this work, we investigate which factors most strongly influence loss-to-loss scaling.
Our experiments reveal that the pretraining data determines the scaling trend.
In contrast, model size, optimization hyperparameters, tokenizer and even significant architectural differences, such as between transformer-based models like Llama and state-space models like Mamba, generally have limited impact.
Consequently, practitioners should carefully curate pretraining datasets for optimal downstream performance, while architectures and other settings can be freely optimized for training efficiency.
|
LLMs, scaling laws, data-centric ML, generalization
|
Pretraining data distribution, rather than model architecture or other factors, determines the loss-to-loss scaling behavior of large language models.
| 12,103 |
2502.12120
|
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] |
https://github.com/brendel-group/llm-line
|
740 |
SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training
|
https://openreview.net/forum?id=CQZXGmw5vO
|
[
"Chao Ma",
"Wenbo Gong",
"Meyer Scetbon",
"Edward Meeds"
] |
Poster
|
Adaptive optimizers such as Adam (Kingma & Ba, 2015) have been central to the success of large language models. However, they often require maintaining optimizer states throughout training, which can result in memory requirements several times greater than the model footprint. This overhead imposes constraints on scalability and computational efficiency. Stochastic Gradient Descent (SGD), in contrast, is a stateless optimizer, as it does not track state variables during training. Consequently, it achieves optimal memory efficiency. However, its capability in LLM training is limited (Zhao et al., 2024b) In this work, we show that pre-processing SGD using normalization and whitening in a stateless manner can achieve similar performance as Adam for LLM training, while maintaining the same memory footprint of SGD. Specifically, we show that normalization stabilizes gradient distributions, and whitening counteracts the local curvature of the loss landscape. This results in SWAN (SGD with Whitening And Normalization), a stochastic optimizer that eliminates the need to store any optimizer states. Empirically, SWAN achieves ~50% reduction on total end-to-end memory compared to Adam. Under the memory-efficienct LLaMA training benchmark of (Zhao et al., 2024a), SWAN reaches the same evaluation perplexity using half as many tokens for 350M and 1.3B model.
|
Efficient LLM optimization, Stateless Optimizers
|
Training LLMs without storing optimizer states
| 12,101 |
2412.13148
|
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] | null |
741 |
Hierarchical Reinforcement Learning with Uncertainty-Guided Diffusional Subgoals
|
https://openreview.net/forum?id=1YOYA2zN1j
|
[
"Vivienne Huiling Wang",
"Tinghuai Wang",
"Joni Pajarinen"
] |
Poster
|
Hierarchical reinforcement learning (HRL) learns to make decisions on multiple levels of temporal abstraction. A key challenge in HRL is that the low-level policy changes over time, making it difficult for the high-level policy to generate effective subgoals. To address this issue, the high-level policy must capture a complex subgoal distribution while also accounting for uncertainty in its estimates. We propose an approach that trains a conditional diffusion model regularized by a Gaussian Process (GP) prior to generate a complex variety of subgoals while leveraging principled GP uncertainty quantification. Building on this framework, we develop a strategy that selects subgoals from both the diffusion policy and GP's predictive mean. Our approach outperforms prior HRL methods in both sample efficiency and performance on challenging continuous control benchmarks.
|
Hierarchical Reinforcement Learning
|
GP-guided diffusion models generate more stable and efficient subgoals in off-policy HRL by combining the diffusion model's flexibility with the GP's uncertainty-aware feasibility, improving performance on complex tasks.
| 12,094 |
2505.21750
|
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] | null |
742 |
Understanding the Logic of Direct Preference Alignment through Logic
|
https://openreview.net/forum?id=4T7gFdhsPJ
|
[
"Kyle Richardson",
"Vivek Srikumar",
"Ashish Sabharwal"
] |
Poster
|
Recent direct preference alignment algorithms (DPA), such as DPO, have shown great promise in aligning large language models to human preferences. While this has motivated the development of many new variants of the original DPO loss, understanding the differences between these recent proposals, as well as developing new DPA loss functions, remains difficult given the lack of a technical and conceptual framework for reasoning about the underlying semantics of these algorithms. In this paper, we attempt to remedy this by formalizing DPA losses in terms of discrete reasoning problems. Specifically, we ask: Given an existing DPA loss, can we systematically derive a symbolic program that characterizes its semantics? We propose a novel formalism for characterizing preference losses for single model and reference model based approaches, and identify symbolic forms for a number of commonly used DPA variants. Further, we show how this formal view of preference learning sheds new light on both the size and structure of the DPA loss landscape, making it possible to not only rigorously characterize the relationships between recent loss proposals but also to systematically explore the landscape and derive new loss functions from first principles. We hope our framework and findings will help provide useful guidance to those working on human AI alignment.
|
preference learning, neuro-symbolic, logic, reasoning, model alignment
|
Formal symbolic characterizations of DPO and preference learning.
| 12,091 |
2412.17696
|
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] | null |
743 |
Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection
|
https://openreview.net/forum?id=vWMij23BmQ
|
[
"Louis Béthune",
"David Grangier",
"Dan Busbridge",
"Eleonora Gualdoni",
"marco cuturi",
"Pierre Ablin"
] |
Poster
|
A widespread strategy to obtain a language model that performs well on a target domain is to finetune a pretrained model to perform unsupervised next-token prediction on data from that target domain.
Finetuning presents two challenges: \textit{(i)} if the amount of target data is limited, as in most practical applications, the model will quickly overfit, and \textit{(ii)} the model will drift away from the original model, forgetting the pretraining data and the generic knowledge that comes with it.
Our goal is to derive scaling laws that quantify these two phenomena for various target domains, amounts of available target data, and model scales.
We measure the efficiency of injecting pretraining data into the finetuning data mixture to avoid forgetting and mitigate overfitting.
A key practical takeaway from our study is that injecting as little as $1\%$ of pretraining data in the finetuning data mixture prevents the model from forgetting the pretraining set.
|
LLM, fine-tuning, scaling laws
|
We study how the loss on pretraining datasets and specialization datasets changes with model size, number of available fine-tuning tokens, and amount of pre-training data injected in the mix during fine-tuning.
| 12,087 |
2502.06042
|
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] | null |
744 |
A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings
|
https://openreview.net/forum?id=27tMzmzDjO
|
[
"Shib Sankar Dasgupta",
"Michael Boratko",
"Andrew McCallum"
] |
Poster
|
Personalized item recommendation typically suffers from data sparsity, which is most often addressed by learning vector representations of users and items via low-rank matrix factorization. While this effectively densifies the matrix by assuming users and movies can be represented by linearly dependent latent features, it does not capture more complicated interactions. For example, vector representations struggle with set-theoretic relationships, such as negation and intersection, e.g. recommending a movie that is “comedy and action, but not romance”. In this work, we formulate the problem of personalized item recommendation as matrix completion where rows are set-theoretically dependent. To capture this set-theoretic dependence we represent each user and attribute by a hyperrectangle or box (i.e. a Cartesian product of intervals). Box embeddings can intuitively be understood as trainable Venn diagrams, and thus not only inherently represent similarity (via the Jaccard index), but also naturally and faithfully support arbitrary set-theoretic relationships. Queries involving set-theoretic constraints can be efficiently computed directly on the embedding space by performing geometric operations on the representations. We empirically demonstrate the superiority of box embeddings over vector-based neural methods on both simple and complex item recommendation queries by up to 30% overall.
|
Box Embeddings, Personalized Query, Set-based embeddings, Recommendation
|
Set-theoretic embeddings offer the appropriate inductive bias needed to effectively answer queries with set constraints.
| 12,081 |
2502.10875
|
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] |
https://github.com/ssdasgupta/set-based-collaborative-filtering
|
745 |
SCENIR: Visual Semantic Clarity through Unsupervised Scene Graph Retrieval
|
https://openreview.net/forum?id=sOUZXKReSN
|
[
"Nikolaos Chaidos",
"Angeliki Dimitriou",
"Maria Lymperaiou",
"Giorgos Stamou"
] |
Poster
|
Despite the dominance of convolutional and transformer-based architectures in image-to-image retrieval, these models are prone to biases arising from low-level visual features, such as color. Recognizing the lack of semantic understanding as a key limitation, we propose a novel scene graph-based retrieval framework that emphasizes semantic content over superficial image characteristics. Prior approaches to scene graph retrieval predominantly rely on supervised Graph Neural Networks (GNNs), which require ground truth graph pairs driven from image captions. However, the inconsistency of caption-based supervision stemming from variable text encodings undermine retrieval reliability.
To address these, we present *SCENIR*, a Graph Autoencoder-based unsupervised retrieval framework, which eliminates the dependence on labeled training data. Our model demonstrates superior performance across metrics and runtime efficiency, outperforming existing vision-based, multimodal, and supervised GNN approaches.
We further advocate for *Graph Edit Distance* (GED) as a deterministic and robust ground truth measure for scene graph similarity, replacing the inconsistent caption-based alternatives for the first time in image-to-image retrieval evaluation.
Finally, we validate the generalizability of our method by applying it to unannotated datasets via automated scene graph generation, while substantially contributing in advancing state-of-the-art in counterfactual image retrieval.
The source code is available at https://github.com/nickhaidos/scenir-icml2025.
|
Scene Graph Retrieval, Unsupervised Graph Autoencoders, Visual Semantic Similarity
|
We present SCENIR, an unsupervised scene graph retrieval framework based on Graph Autoencoders, to efficiently tackle semantic image similarity.
| 12,072 |
2505.15867
|
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] |
https://github.com/nickhaidos/scenir-icml2025
|
746 |
Temporal Misalignment in ANN-SNN Conversion and its Mitigation via Probabilistic Spiking Neurons
|
https://openreview.net/forum?id=Kip4avTjth
|
[
"Velibor Bojkovic",
"Xiaofeng Wu",
"Bin Gu"
] |
Poster
|
Spiking Neural Networks (SNNs) offer a more energy-efficient alternative to Artificial Neural Networks (ANNs) by mimicking biological neural principles, establishing them as a promising approach to mitigate the increasing energy demands of large-scale neural models. However, fully harnessing the capabilities of SNNs remains challenging due to their discrete signal processing and temporal dynamics. ANN-SNN conversion has emerged as a practical approach, enabling SNNs to achieve competitive performance on complex machine learning tasks. In this work, we identify a phenomenon in the ANN-SNN conversion framework, termed *temporal misalignment*, in which random spike rearrangement across SNN layers leads to performance improvements. Based on this observation, we introduce biologically plausible two-phase probabilistic (TPP) spiking neurons, further enhancing the conversion process. We demonstrate the advantages of our proposed method both theoretically and empirically through comprehensive experiments on CIFAR-10/100, CIFAR10-DVS, and ImageNet across a variety of architectures, achieving state-of-the-art results.
|
Spiking Neural Networks, SNN, ANN-SNN conversion, probabilistic spiking
|
Identification of a phenomenon in ANN-SNN conversion and its mitigation through novel approach in ANN-SNN conversion.
| 12,057 |
2502.14487
|
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] |
https://github.com/shizukanaskytree/TPP-SNN
|
747 |
Active Reward Modeling: Adaptive Preference Labeling for Large Language Model Alignment
|
https://openreview.net/forum?id=GSyX4amBFR
|
[
"Yunyi Shen",
"Hao Sun",
"Jean-Francois Ton"
] |
Poster
|
Building neural reward models from human preferences is a pivotal component in reinforcement learning from human feedback (RLHF) and large language model alignment research. Given the scarcity and high cost of human annotation, how to select the most informative pairs to annotate is an essential yet challenging open problem. In this work, we highlight the insight that an ideal comparison dataset for reward modeling should balance *exploration of the representation space* and make *informative comparisons* between pairs with moderate reward differences. Technically, challenges arise in quantifying the two objectives and efficiently prioritizing the comparisons to be annotated. To address this, we propose the Fisher information-based selection strategies, adapt theories from the *classical experimental design* literature, and apply them to the final linear layer of the deep neural network-based reward modeling tasks. Empirically, our method demonstrates remarkable performance, high computational efficiency, and stability compared to other selection methods from deep learning and classical statistical literature across multiple open-source LLMs and datasets. Further ablation studies reveal that incorporating cross-prompt comparisons in active reward modeling significantly enhances labeling efficiency, shedding light on the potential for improved annotation strategies in RLHF. Code and embeddings to reproduce all results of this paper are available at https://github.com/YunyiShen/ARM-FI/.
|
Reward modeling, active learning, LLM alignment
|
We propose a last-layer Fisher information-based method to choose comparisons for reward modeling.
| 12,049 | null |
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] |
https://github.com/YunyiShen/ARM-FI
|
748 |
Improved and Oracle-Efficient Online ℓ1-Multicalibration
|
https://openreview.net/forum?id=Qu3jbojB5Q
|
[
"Rohan Ghuge",
"Vidya Muthukumar",
"Sahil Singla"
] |
Poster
|
We study *online multicalibration*, a framework for ensuring calibrated predictions across multiple groups in adversarial settings, across $T$ rounds. Although online calibration is typically studied in the $\ell_1$ norm, prior approaches to online multicalibration have taken the indirect approach of obtaining rates in other norms (such as $\ell_2$ and $\ell_{\infty}$) and then transferred these guarantees to $\ell_1$ at additional loss. In contrast, we propose a direct method that achieves improved and oracle-efficient rates of $\widetilde{\mathcal{O}}(T^{-1/3})$ and $\widetilde{\mathcal{O}}(T^{-1/4})$ respectively, for online $\ell_1$-multicalibration. Our key insight is a novel reduction of online $\ell_1$-multicalibration to an online learning problem with product-based rewards, which we refer to as *online linear-product optimization* ($\mathtt{OLPO}$).
To obtain the improved rate of $\widetilde{\mathcal{O}}(T^{-1/3})$, we introduce a linearization of $\mathtt{OLPO}$ and design a no-regret algorithm for this linearized problem. Although this method guarantees the desired sublinear rate (nearly matching the best rate for online calibration), it is computationally expensive when the group family $\mathcal{H}$ is large or infinite, since it enumerates all possible groups. To address scalability, we propose a second approach to $\mathtt{OLPO}$ that makes only a polynomial number of calls to an offline optimization (*multicalibration evaluation*) oracle, resulting in *oracle-efficient* online $\ell_1$-multicalibration with a corresponding rate of $\widetilde{\mathcal{O}}(T^{-1/4})$. Our framework also extends to certain infinite families of groups (e.g., all linear functions on the context space) by exploiting a $1$-Lipschitz property of the $\ell_1$-multicalibration error with respect to $\mathcal{H}$.
|
online multicalibration, online learning, oracle-efficient algorithms
| null | 12,045 | null |
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] | null |
749 |
Partially Observable Reinforcement Learning with Memory Traces
|
https://openreview.net/forum?id=c4zVRwxjDD
|
[
"Onno Eberhard",
"Michael Muehlebach",
"Claire Vernade"
] |
Poster
|
Partially observable environments present a considerable computational challenge in reinforcement learning due to the need to consider long histories. Learning with a finite window of observations quickly becomes intractable as the window length grows. In this work, we introduce *memory traces*. Inspired by eligibility traces, these are compact representations of the history of observations in the form of exponential moving averages. We prove sample complexity bounds for the problem of offline on-policy evaluation that quantify the return errors achieved with memory traces for the class of Lipschitz continuous value estimates. We establish a close connection to the window approach, and demonstrate that, in certain environments, learning with memory traces is significantly more sample efficient. Finally, we underline the effectiveness of memory traces empirically in online reinforcement learning experiments for both value prediction and control.
|
Reinforcement learning theory, Partial observability, Memory
|
Eligibility traces are more effective than sliding windows as a memory mechanism for RL in POMDPs.
| 12,039 |
2503.15200
|
[
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] |
https://github.com/onnoeberhard/memory-traces
|
750 |
AutoCATE: End-to-End, Automated Treatment Effect Estimation
|
https://openreview.net/forum?id=QbOoz74GNO
|
[
"Toon Vanderschueren",
"Tim Verdonck",
"Mihaela van der Schaar",
"Wouter Verbeke"
] |
Poster
|
Estimating causal effects is crucial in domains like healthcare, economics, and education. Despite advances in machine learning (ML) for estimating conditional average treatment effects (CATE), the practical adoption of these methods remains limited, due to the complexities of implementing, tuning, and validating them. To address these challenges, we formalize the search for an optimal ML pipeline for CATE estimation as a counterfactual Combined Algorithm Selection and Hyperparameter (CASH) optimization. We introduce AutoCATE, the first end-to-end, automated solution for CATE estimation. Unlike prior approaches that address only parts of this problem, AutoCATE integrates evaluation, estimation, and ensembling in a unified framework. AutoCATE enables comprehensive comparisons of different protocols, yielding novel insights into CATE estimation and a final configuration that outperforms commonly used strategies. To facilitate broad adoption and further research, we release AutoCATE as an open-source software package.
|
Treatment Effect Estimation, Causal Inference, AutoML
|
AutoCATE is the first end-to-end, automated framework for estimating conditional average treatment effects (CATE), unifying evaluation, estimation, and ensembling in a single framework.
| 12,026 | null |
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] |
https://github.com/toonvds/AutoCATE
|
751 |
Provable Length Generalization in Sequence Prediction via Spectral Filtering
|
https://openreview.net/forum?id=AVFUoC53Lm
|
[
"Annie Marsden",
"Evan Dogariu",
"Naman Agarwal",
"Xinyi Chen",
"Daniel Suo",
"Elad Hazan"
] |
Poster
|
We consider the problem of length generalization in sequence prediction. We define a new metric of performance in this setting – the Asymmetric-Regret– which measures regret against a benchmark predictor with longer context length than available to the learner. We continue by studying this concept through the lens of the spectral filter-ing algorithm. We present a gradient-based learn-ing algorithm that provably achieves length generalization for linear dynamical systems. We conclude with proof-of-concept experiments which are consistent with our theory.
|
sequence prediction; length generalization
|
We define a measure of length generalization and provide provable guarantees based on this notion for the spectral filtering algorithm.
| 12,021 |
2411.01035
|
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] | null |
752 |
Exploring Large Action Sets with Hyperspherical Embeddings using von Mises-Fisher Sampling
|
https://openreview.net/forum?id=Fk8yB6uSJy
|
[
"Walid Bendada",
"Guillaume Salha-Galvan",
"Romain Hennequin",
"Théo Bontempelli",
"Thomas Bouabça",
"Tristan Cazenave"
] |
Poster
|
This paper introduces von Mises-Fisher exploration (vMF-exp), a scalable method for exploring large action sets in reinforcement learning problems where hyperspherical embedding vectors represent these actions. vMF-exp involves initially sampling a state embedding representation using a von Mises-Fisher distribution, then exploring this representation's nearest neighbors, which scales to virtually unlimited numbers of candidate actions.
We show that, under theoretical assumptions, vMF-exp asymptotically maintains the same probability of exploring each action as Boltzmann Exploration (B-exp), a popular alternative that, nonetheless, suffers from scalability issues as it requires computing softmax values for each action.
Consequently, vMF-exp serves as a scalable alternative to B-exp for exploring large action sets with hyperspherical embeddings.
Experiments on simulated data, real-world public data, and the successful large-scale deployment of vMF-exp on the recommender system of a global music streaming service empirically validate the key properties of the proposed method.
|
Efficient Sampling, Probability Theory, Large-Scale, Embeddings, Real-World Application, Reinforcement Learning
|
When action sets are too large, it is not possible to sample from a softmax distribution in real-time ; we propose a new scalable approach to sample actions represented by embeddings and mathematically prove its ties to Boltzmann exploration.
| 12,018 |
2507.00518
|
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] |
https://github.com/deezer/vMF-exploration
|
753 |
DIME: Diffusion-Based Maximum Entropy Reinforcement Learning
|
https://openreview.net/forum?id=Aw6dBR7Vxj
|
[
"Onur Celik",
"Zechu Li",
"Denis Blessing",
"Ge Li",
"Daniel Palenicek",
"Jan Peters",
"Georgia Chalvatzaki",
"Gerhard Neumann"
] |
Poster
|
Maximum entropy reinforcement learning (MaxEnt-RL) has become the standard approach to RL due to its beneficial exploration properties. Traditionally, policies are parameterized using Gaussian distributions, which significantly limits their representational capacity. Diffusion-based policies offer a more expressive alternative, yet integrating them into MaxEnt-RL poses challenges—primarily due to the intractability of computing their marginal entropy.
To overcome this, we propose Diffusion-Based Maximum Entropy RL (DIME). DIME leverages recent advances in approximate inference with diffusion models to derive a lower bound on the maximum entropy objective.
Additionally, we propose a policy iteration scheme that provably converges to the optimal diffusion policy. Our method enables the use of expressive diffusion-based policies while retaining the principled exploration benefits of MaxEnt-RL, significantly outperforming other diffusion-based methods on challenging high-dimensional control benchmarks. It is also competitive with state-of-the-art non-diffusion based RL methods while requiring fewer algorithmic design choices and smaller update-to-data ratios, reducing computational complexity.
|
Reinforcement Learning, Diffusion Models, Diffusion Based Reinforcement Learning, Maximum Entropy Reinforcement Learning
|
We provide DIME, a method for training diffusion-based policies in the maximum entropy reinforcement learning framework.
| 12,014 |
2502.02316
|
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] | null |
754 |
Distillation Scaling Laws
|
https://openreview.net/forum?id=1nEBAkpfb9
|
[
"Dan Busbridge",
"Amitis Shidani",
"Floris Weers",
"Jason Ramapuram",
"Etai Littwin",
"Russell Webb"
] |
Poster
|
We propose a distillation scaling law that estimates distilled model performance based on a compute budget and its allocation between the student and teacher. Our findings mitigate the risks associated with large-scale distillation by enabling compute-optimal allocation for both the teacher and student to maximize student performance. We provide compute-optimal distillation recipes for two key scenarios: when a teacher already exists, and when a teacher needs training. In settings involving many students or an existing teacher, distillation outperforms supervised learning up to a compute level that scales predictably with student size. Conversely, if only one student is to be distilled and a teacher also requires training, supervised learning is generally preferable. Additionally, our large-scale study of distillation increases our understanding of the process and helps inform experimental design.
|
scaling laws, distillation, pretraining, alms, large language models
|
We provide a distillation scaling law that estimates distilled model performance based on a compute budget and its allocation between the student and teacher.
| 12,010 |
2502.08606
|
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] | null |
755 |
Inverse Flow and Consistency Models
|
https://openreview.net/forum?id=DpPUIOPY7C
|
[
"Yuchen Zhang",
"Jian Zhou"
] |
Poster
|
Inverse generation problems, such as denoising without ground truth observations, is a critical challenge in many scientific inquiries and real-world applications. While recent advances in generative models like diffusion models, conditional flow matching, and consistency models achieved impressive results by casting generation as denoising problems, they cannot be directly used for inverse generation without access to clean data. Here we introduce Inverse Flow (IF), a novel framework that enables using these generative models for inverse generation problems including denoising without ground truth. Inverse Flow can be flexibly applied to nearly any continuous noise distribution and allows complex dependencies. We propose two algorithms for learning Inverse Flows, Inverse Flow Matching (IFM) and Inverse Consistency Model (ICM). Notably, to derive the computationally efficient, simulation-free inverse consistency model objective, we generalized consistency training to any forward diffusion processes or conditional flows, which have applications beyond denoising. We demonstrate the effectiveness of IF on synthetic and real datasets, outperforming prior approaches while enabling noise distributions that previous methods cannot support. Finally, we showcase applications of our techniques to fluorescence microscopy and single-cell genomics data, highlighting IF's utility in scientific problems. Overall, this work expands the applications of powerful generative models to inversion generation problems.
|
consistency models, flow matching, diffusion models, inverse problem
| null | 12,002 |
2502.11333
|
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] | null |
756 |
A Sub-Problem Quantum Alternating Operator Ansatz for Correlation Clustering
|
https://openreview.net/forum?id=UcnL2Xd3Qm
|
[
"Lucas Fabian Naumann",
"Jannik Irmai",
"Bjoern Andres"
] |
Poster
|
The Quantum Alternating Operator Ansatz (QAOA) is a hybrid quantum-classical variational algorithm for approximately solving combinatorial optimization problems on Noisy Intermediate-Scale Quantum (NISQ) devices. Although it has been successfully applied to a variety of problems, there is only limited work on correlation clustering due to the difficulty of modelling the problem constraints with the ansatz. Motivated by this, we present a generalization of QAOA that is more suitable for this problem. In particular, we modify QAOA in two ways: Firstly, we use nucleus sampling for the computation of the expected cost. Secondly, we split the problem into sub-problems, solving each individually with QAOA. We call this generalization the Sub-Problem Quantum Alternating Operator Ansatz (SQAOA) and show theoretically that optimal solutions for correlation clustering instances can be obtained with certainty when the depth of the ansatz tends to infinity. Further, we show experimentally that SQAOA achieves better approximation ratios than QAOA for correlation clustering, while using only one qubit per node of the respective problem instance and reducing the runtime (of simulations).
|
Quantum Computing, QAOA, Correlation Clustering
|
A generalization of the Quanutm Alternating Operator Ansatz suitable for correlation clustering.
| 12,000 | null |
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] |
https://github.com/fabian-na/SQAOA
|
757 |
Modular Duality in Deep Learning
|
https://openreview.net/forum?id=hErdffTsLu
|
[
"Jeremy Bernstein",
"Laker Newhouse"
] |
Poster
|
An old idea in optimization theory says that since the gradient is a dual vector it may not be subtracted from the weights without first being mapped to the primal space where the weights reside. We take this idea seriously in this paper and construct such a duality map for general neural networks. Our map, which we call modular dualization, forms a unifying theoretical basis for training algorithms that are a) fast and b) scalable. Modular dualization involves first assigning operator norms to layers based on the semantics of each layer, and then using these layerwise norms to recursively induce a duality map on the weight space of the full neural architecture. We derive GPU-friendly algorithms for dualizing Embed, Linear and Conv2D layers—the latter two methods are based on a Newton-Schulz iteration. We conclude with small experiments demonstrating the speed, scalability and novel numerical properties of duality-based optimizers. Our methods were used in the Muon optimizer, which recently set speed records for training NanoGPT and was scaled up to a 1.5 billion parameter transformer.
|
modular, duality, Newton-Schulz
|
Modular dualization maps gradients to weight space in neural nets, enabling fast and scalable training algorithms automatically optimized for different architectures. Successfully used by the community to speed up NanoGPT training.
| 11,992 |
2410.21265
|
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] | null |
758 |
Differential Coding for Training-Free ANN-to-SNN Conversion
|
https://openreview.net/forum?id=OxBWTFSGcv
|
[
"Zihan Huang",
"Wei Fang",
"Tong Bu",
"Peng Xue",
"Zecheng Hao",
"Wenxuan Liu",
"Yuanhong Tang",
"Zhaofei Yu",
"Tiejun Huang"
] |
Poster
|
Spiking Neural Networks (SNNs) exhibit significant potential due to their low energy consumption. Converting Artificial Neural Networks (ANNs) to SNNs is an efficient way to achieve high-performance SNNs. However, many conversion methods are based on rate coding, which requires numerous spikes and longer time-steps compared to directly trained SNNs, leading to increased energy consumption and latency. This article introduces differential coding for ANN-to-SNN conversion, a novel coding scheme that reduces spike counts and energy consumption by transmitting changes in rate information rather than rates directly, and explores its application across various layers. Additionally, the threshold iteration method is proposed to optimize thresholds based on activation distribution when converting Rectified Linear Units (ReLUs) to spiking neurons. Experimental results on various Convolutional Neural Networks (CNNs) and Transformers demonstrate that the proposed differential coding significantly improves accuracy while reducing energy consumption, particularly when combined with the threshold iteration method, achieving state-of-the-art performance. The source codes of the proposed method are available at https://github.com/h-z-h-cell/ANN-to-SNN-DCGS.
|
Spiking Neural Networks, ANN-to-SNN Covnersion, Differential Coding, Threshold Iteration
| null | 11,985 |
2503.00301
|
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] |
https://github.com/h-z-h-cell/ANN-to-SNN-DCGS
|
759 |
AlphaDPO: Adaptive Reward Margin for Direct Preference Optimization
|
https://openreview.net/forum?id=ETLKYVMVLt
|
[
"Junkang Wu",
"Xue Wang",
"Zhengyi Yang",
"Jiancan Wu",
"Jinyang Gao",
"Bolin Ding",
"Xiang Wang",
"Xiangnan He"
] |
Poster
|
Aligning large language models (LLMs) with human preferences requires balancing policy optimization with computational stability. While recent offline methods like DPO and SimPO bypass reinforcement learning’s complexity, they face critical limitations: DPO relies on static reference models that degrade with policy updates, and SimPO assumes a uniform target reward margin that ignores instance-wise preference strength. We propose AlphaDPO, an adaptive preference optimization framework that dynamically reparameterizes the reference distribution to address these issues. Our key innovation lies in an implicit reference model \(\hat{\pi}_{\text{ref}} \propto U(y|x)(\pi_\theta/\pi_{\text{ref}})^\alpha\), which interpolates between policy-driven specialization and uniform exploration while enabling instance-adaptive reward margins. Theoretically, we prove AlphaDPO implicitly controls sequential KL divergence between iterative policy updates, ensuring stability even with poorly calibrated reference models. Empirically, AlphaDPO achieves state-of-the-art performance on AlpacaEval 2 (58.7\% LC win rate) and Arena-Hard (35.7\% win rate) across Mistral2-7B, Llama3-8B, and Gemma2-9B, demonstrating robust alignment without multi-stage training. Our work establishes adaptive reference reparameterization as a principled mechanism for preference optimization.
|
Direct Preference Optimization, LLM's alignment
| null | 11,970 | null |
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] | null |
760 |
Prompt-to-Leaderboard: Prompt-Adaptive LLM Evaluations
|
https://openreview.net/forum?id=7VPRrzFEN8
|
[
"Evan Frick",
"Connor Chen",
"Joseph Tennyson",
"Tianle Li",
"Wei-Lin Chiang",
"Anastasios Nikolas Angelopoulos",
"Ion Stoica"
] |
Poster
|
Large language model (LLM) evaluations typically rely on aggregated metrics like accuracy or human preference, averaging across users and prompts. This averaging obscures user- and prompt-specific variations in model performance. To address this, we propose Prompt-to-Leaderboard (P2L), a method that produces leaderboards specific to a prompt or set of prompts. The core idea is to train an LLM taking natural language prompts as input to output a vector of Bradley-Terry coefficients which are then used to predict the human preference vote. The resulting prompt-dependent leaderboards allow for unsupervised task-specific evaluation, optimal routing of queries to models, personalization, and automated evaluation of model strengths and weaknesses. Data from Chatbot Arena suggest that P2L better captures the nuanced landscape of language model performance than the averaged leaderboard. Furthermore, our findings suggest that P2L's ability to produce prompt-specific evaluations follows a power law scaling similar to that observed in LLMs themselves. In January 2025, the router we trained based on this methodology achieved the \#1 spot on the Chatbot Arena leaderboard. Our code is available at this GitHub link: https://github.com/lmarena/p2l.
|
llm, leaderboard, evaluations
| null | 11,964 | null |
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] |
https://github.com/lmarena/p2l
|
761 |
∞-Video: A Training-Free Approach to Long Video Understanding via Continuous-Time Memory Consolidation
|
https://openreview.net/forum?id=afDHwQ1ZDO
|
[
"Saul Santos",
"António Farinhas",
"Daniel C McNamee",
"Andre Martins"
] |
Poster
|
Current video-language models struggle with long-video understanding due to limited context lengths and reliance on sparse frame subsampling, which often leads to information loss. In this paper, we introduce $\infty$-Video, which is able to process arbitrarily long videos through a continuous-time long-term memory (LTM) consolidation mechanism. Our framework augments video Q-formers by making them able to process unbounded video contexts efficiently and without requiring additional training. Through continuous attention, our approach dynamically allocates higher granularity to the most relevant video segments, forming "sticky" memories which evolve over time. Experiments with Video-LLaMA and VideoChat2 demonstrate improved performance in video question-answering tasks, showcasing the potential of continuous-time LTM mechanisms to enable scalable and training-free comprehension of long videos.
|
Long context, Video understanding, LLM, Continuous attention, VLLM
| null | 11,959 |
2501.19098
|
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] |
https://github.com/deep-spin/Infinite-Video
|
762 |
Physics-Informed Generative Modeling of Wireless Channels
|
https://openreview.net/forum?id=FFJFT93oa7
|
[
"Benedikt Böck",
"Andreas Oeldemann",
"Timo Mayer",
"Francesco Rossetto",
"Wolfgang Utschick"
] |
Poster
|
Learning the site-specific distribution of the wireless channel within a particular environment of interest is essential to exploit the full potential of machine learning (ML) for wireless communications and radar applications. Generative modeling offers a promising framework to address this problem. However, existing approaches pose unresolved challenges, including the need for high-quality training data, limited generalizability, and a lack of physical interpretability. To address these issues, we combine the physics-related compressibility of wireless channels with generative modeling, in particular, sparse Bayesian generative modeling (SBGM), to learn the distribution of the underlying physical channel parameters. By leveraging the sparsity-inducing characteristics of SBGM, our methods can learn from compressed observations received by an access point (AP) during default online operation. Moreover, they are physically interpretable and generalize over system configurations without requiring retraining.
|
sparse Bayesian generative modeling, wireless channel modeling, physics-informed, generative model
|
We propose a physically interpretable and generalizable generative model for wireless channels that can learn from corrupted channel observations.
| 11,946 |
2502.10137
|
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] | null |
763 |
Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing
|
https://openreview.net/forum?id=vOu5K93z4f
|
[
"Tianci Liu",
"Ruirui Li",
"Zihan Dong",
"Hui Liu",
"Xianfeng Tang",
"Qingyu Yin",
"Linjun Zhang",
"Haoyu Wang",
"Jing Gao"
] |
Poster
|
Large language models (LLMs) have achieved remarkable performance on various natural language tasks. However, they are trained on static corpora and their knowledge can become outdated quickly in the fast-changing world.
This motivates the development of knowledge editing (KE) to update specific knowledge in LLMs without changing unrelated others or compromising their pre-trained capabilities.
Previous efforts sought to update a small amount of parameters of a LLM and proved effective for making selective updates.
Nonetheless, the edited LLM often exhibits degraded ability to reason about the new knowledge.
In this work, we identify a key issue: \textit{heterogeneous token overfitting} (HTO), where the LLM overfits different tokens in the provided knowledge at varying rates.
To tackle this, we propose {OVERTONE}, a token-level smoothing method that mitigates HTO by adaptively refining the target distribution.
Theoretically, OVERTONE offers better parameter updates with negligible computation overhead.
It also induces an implicit DPO but does not require preference data pairs.
Extensive experiments across four editing methods, two LLMs, and diverse scenarios demonstrate the effectiveness and versatility of our method.
|
Knowledge Editing, Large Language Models
| null | 11,933 |
2502.00602
|
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] | null |
764 |
Improved Theoretically-Grounded Evolutionary Algorithms for Subset Selection with a Linear Cost Constraint
|
https://openreview.net/forum?id=Mcae98J1wq
|
[
"Dan-Xuan Liu",
"Chao Qian"
] |
Poster
|
The subset selection problem with a monotone and submodular objective function under a linear cost constraint has wide applications, such as maximum coverage, influence maximization, and feature selection, just to name a few. Various greedy algorithms have been proposed with good performance both theoretically and empirically. Recently, evolutionary algorithms (EAs), inspired by Darwin's evolution theory, have emerged as a prominent methodology, offering both empirical advantages and theoretical guarantees. Among these, the multi-objective EA, POMC, has demonstrated the best empirical performance to date, achieving an approximation guarantee of $(1/2)(1-1/e)$. However, there remains a gap in the approximation bounds of EAs compared to greedy algorithms, and their full theoretical potential is yet to be realized. In this paper, we re-analyze the approximation performance of POMC theoretically, and derive an improved guarantee of $1/2$, which thus provides theoretical justification for its encouraging empirical performance. Furthermore, we propose a novel multi-objective EA, EPOL, which not only achieves the best-known practical approximation guarantee of $0.6174$, but also delivers superior empirical performance in applications of maximum coverage and influence maximization. We hope this work can help better solving the subset selection problem, but also enhance our theoretical understanding of EAs.
|
submodular optimization, linear constraints, multi-objective evolutionary algorithms
| null | 11,929 | null |
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] |
https://github.com/lamda-bbo/EPOL
|
765 |
Private Model Personalization Revisited
|
https://openreview.net/forum?id=hw1kGPcSZ5
|
[
"Conor Snedeker",
"Xinyu Zhou",
"Raef Bassily"
] |
Poster
|
We study model personalization under user-level differential privacy (DP) in the shared representation framework. In this problem, there are $n$ users whose data is statistically heterogeneous, and their optimal parameters share an unknown embedding $U^* \in\mathbb{R}^{d\times k}$ that maps the user parameters in $\mathbb{R}^d$ to low-dimensional representations in $\mathbb{R}^k$, where $k\ll d$. Our goal is to privately recover the shared embedding and the local low-dimensional representations with small excess risk in the federated setting. We propose a private, efficient federated learning algorithm to learn the shared embedding based on the FedRep algorithm in (Collins et al.,
2021). Unlike (Collins et al., 2021), our algorithm satisfies differential privacy, and our results hold for the case of noisy labels. In contrast to prior work on private model personalization (Jain et al., 2021), our utility guarantees hold under a larger class of users' distributions (sub-Gaussian instead of Gaussian distributions). Additionally, in natural parameter regimes, we improve the privacy error term in (Jain
et al., 2021) by a factor of $\widetilde{O}(dk)$. Next, we consider the binary classification setting. We present an information-theoretic construction to privately learn the shared embedding and derive a margin-based accuracy guarantee that is independent of $d$. Our method utilizes the Johnson-Lindenstrauss transform to reduce the effective dimensions of the shared embedding and the users' data. This result shows that dimension-independent risk bounds are possible in this setting under a margin loss.
|
Differential Privacy, Model Personalization, Federated/Multi-task Learning, Representation Learning, Statistical Heterogeneity
|
We propose model personalization algorithms under user-level differential privacy via shared representation learning.
| 11,924 |
2506.19220
|
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] | null |
766 |
Larger or Smaller Reward Margins to Select Preferences for LLM Alignment?
|
https://openreview.net/forum?id=ncTwQagrj8
|
[
"Kexin Huang",
"Junkang Wu",
"Ziqian Chen",
"Xue Wang",
"Jinyang Gao",
"Bolin Ding",
"Jiancan Wu",
"Xiangnan He",
"Xiang Wang"
] |
Poster
|
Preference learning is critical for aligning large language models (LLMs) with human values, with the quality of preference datasets playing a crucial role in this process.
While existing metrics primarily assess data quality based on either *explicit* or *implicit* reward margins, their single-margin focus often leads to contradictory evaluations for the same data.
To address this issue, we propose a new metric of *alignment potential*, $M_{AP}$,
which integrates both margins to quantify
the gap from the model's *current implicit* reward margin to the *target explicit* reward margin, thereby estimating the model's potential to align on the preference data.
Empirical results demonstrate that training on the data selected by $M_{AP}$ consistently enhances alignment performance, surpassing existing metrics across different base models and optimization objectives.
Furthermore, our method can be extended to self-play data generation frameworks, where we use this metric to identify high-quality data within the self-generated content by LLMs.
Under this data generation scenario, our method surpasses current state-of-the-art
methods across various training settings and demonstrates continuous improvements
with increasing dataset size and training iterations.
|
LLM, RLHF, Preference Optimization, Data Quality
| null | 11,915 | null |
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] |
https://github.com/Hesse73/Alignment-Potential-Metric
|
767 |
Learning Robust Neural Processes with Risk-Averse Stochastic Optimization
|
https://openreview.net/forum?id=HZdK1aj22X
|
[
"Huafeng Liu",
"Yiran Fu",
"Liping Jing",
"Hui Li",
"Shuyang Lin",
"Jingyue Shi",
"Deqiang Ouyang",
"Jian Yu"
] |
Poster
|
Neural processes (NPs) are a promising paradigm to enable skill transfer learning across tasks with the aid of the distribution of functions. The previous NPs employ the empirical risk minimization principle in optimization. However, the fast adaption ability to different tasks can vary widely, and the worst fast adaptation can be catastrophic in risk-sensitive tasks. To achieve robust neural processes modeling, we consider the problem of training models in a risk-averse manner, which can control the worst fast adaption cases at a certain probabilistic level. By transferring the risk minimization problem to a two-level finite sum minimax optimization problem, we can easily solve it via a double-looped stochastic mirror prox algorithm with a task-aware variance reduction mechanism via sampling samples across all tasks. The mirror prox technique ensures better handling of complex constraint sets and non-Euclidean geometries, making the optimization adaptable to various tasks. The final solution, by aggregating prox points with the adaptive learning rates, enables a stable and high-quality output. The proposed learning strategy can work with various NPs flexibly and achieves less biased approximation with a theoretical guarantee. To illustrate the superiority of the proposed model, we perform experiments on both synthetic and real-world data, and the results demonstrate that our approach not only helps to achieve more accurate performance but also improves model robustness.
|
neural process, robust learning
| null | 11,914 | null |
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] | null |
768 |
PertEval-scFM: Benchmarking Single-Cell Foundation Models for Perturbation Effect Prediction
|
https://openreview.net/forum?id=t04D9bkKUq
|
[
"Aaron Wenteler",
"Martina Occhetta",
"Nikhil Branson",
"Victor Curean",
"Magdalena Huebner",
"William Dee",
"William Connell",
"Siu Pui Chung",
"Alex Hawkins-Hooker",
"Yasha Ektefaie",
"César Miguel Valdez Córdova",
"Amaya Gallagher-Syed"
] |
Poster
|
*In silico* modeling of transcriptional responses to perturbations is crucial for advancing our understanding of cellular processes and disease mechanisms. We present PertEval-scFM, a standardized framework designed to evaluate models for perturbation effect prediction. We apply PertEval-scFM to benchmark zero-shot single-cell foundation model (scFM) embeddings against baseline models to assess whether these contextualized representations enhance perturbation effect prediction. Our results show that scFM embeddings offer limited improvement over simple baseline models in the zero-shot setting, particularly under distribution shift. Overall, this study provides a systematic evaluation of zero-shot scFM embeddings for perturbation effect prediction, highlighting the challenges of this task and the limitations of current-generation scFMs. Our findings underscore the need for specialized models and high-quality datasets that capture a broader range of cellular states. Source code and documentation can be found at: https://github.com/aaronwtr/PertEval.
|
benchmark, single-cell biology, perturbation effect prediction
|
Using PertEval-scFM, we benchmark zero-shot single-cell foundation model (scFM) embeddings and find that they do not outperform simpler baselines for perturbation effect prediction.
| 11,903 | null |
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] |
https://github.com/aaronwtr/PertEval
|
769 |
Vintix: Action Model via In-Context Reinforcement Learning
|
https://openreview.net/forum?id=gi9MOXNfw2
|
[
"Andrei Polubarov",
"Lyubaykin Nikita",
"Alexander Derevyagin",
"Ilya Zisman",
"Denis Tarasov",
"Alexander Nikulin",
"Vladislav Kurenkov"
] |
Poster
|
In-Context Reinforcement Learning (ICRL) represents a promising paradigm for developing generalist agents that learn at inference time through trial-and-error interactions, analogous to how large language models adapt contextually, but with a focus on reward maximization. However, the scalability of ICRL beyond toy tasks and single-domain settings remains an open challenge. In this work, we present the first steps toward scaling ICRL by introducing a fixed, cross-domain model capable of learning behaviors through in-context reinforcement learning. Our results demonstrate that Algorithm Distillation, a framework designed to facilitate ICRL, offers a compelling and competitive alternative to expert distillation to construct versatile action models. These findings highlight the potential of ICRL as a scalable approach for generalist decision-making systems.
|
in-context reinforcement learning
|
A cross-domain action model pre-trained directly for In-Context Reinforcement Learning
| 11,900 |
2501.19400
|
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] |
https://github.com/dunnolab/vintix
|
770 |
MetricEmbedding: Accelerate Metric Nearness by Tropical Inner Product
|
https://openreview.net/forum?id=BuTbVl9abf
|
[
"Muyang Cao",
"Jiajun Yu",
"Xin Du",
"Gang Pan",
"Wei Wang"
] |
Poster
|
The Metric Nearness Problem involves restoring a non-metric matrix to its closest metric-compliant form, addressing issues such as noise, missing values, and data inconsistencies. Ensuring metric properties, particularly the $O(N^3)$ triangle inequality constraints, presents significant computational challenges, especially in large-scale scenarios where traditional methods suffer from high time and space complexity. We propose a novel solution based on the tropical inner product (max-plus operation), which we prove satisfies the triangle inequality for non-negative real matrices. By transforming the problem into a continuous optimization task, our method directly minimizes the distance to the target matrix. This approach not only restores metric properties but also generates metric-preserving embeddings, enabling real-time updates and reducing computational and storage overhead for downstream tasks. Experimental results demonstrate that our method achieves up to 60× speed improvements over state-of-the-art approaches, and efficiently scales from $1e4 \times 1e4$ to $1e5 \times 1e5$ matrices with significantly lower memory usage.
|
metric nearness problem, metric, triangle inequality, tropical inner product
|
We propose MetricEmbedding, which accelerates the Metric Nearness Problem using tropical inner product, achieving up to 60× faster computation and significantly reducing memory usage while preserving metric properties in large-scale matrices
| 11,898 | null |
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] | null |
771 |
Unified Breakdown Analysis for Byzantine Robust Gossip
|
https://openreview.net/forum?id=OOYPeymDz2
|
[
"Renaud Gaucher",
"Aymeric Dieuleveut",
"Hadrien Hendrikx"
] |
Poster
|
In decentralized machine learning, different devices communicate in a peer-to-peer manner to collaboratively learn from each other's data. Such approaches are vulnerable to misbehaving (or Byzantine) devices.
We introduce F-RG, a general framework for building robust decentralized algorithms with guarantees arising from robust-sum-like aggregation rules F. We then investigate the notion of *breakdown point*, and show an upper bound on the number of adversaries that decentralized algorithms can tolerate. We introduce a practical robust aggregation rule, coined CS+, such that CS+-RG has a near-optimal breakdown. Other choices of aggregation rules lead to existing algorithms such as ClippedGossip or NNA. We give experimental evidence to validate the effectiveness of CS+-RG and highlight the gap with NNA, in particular against a novel attack tailored to decentralized communications.
|
Byzantine, robustness, decentralized, distributed, optimization, gossip, breakdown
|
A new analysis for Byzantine-robust gossip, with tight breakdown and convergence guarantees.
| 11,893 |
2410.10418
|
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] |
https://github.com/renaudgaucher/Byzantine-Robust-Gossip
|
772 |
SkipGPT: Each Token is One of a Kind
|
https://openreview.net/forum?id=d7v2iUSa9s
|
[
"Anhao Zhao",
"Fanghua Ye",
"Yingqi Fan",
"Junlong Tong",
"Jing Xiong",
"Zhiwei Fei",
"Hui Su",
"Xiaoyu Shen"
] |
Poster
|
Large language models (LLMs) achieve remarkable performance across tasks but incur substantial computational costs due to their deep, multi-layered architectures. Layer pruning has emerged as a strategy to alleviate these inefficiencies, but conventional static pruning methods overlook two critical dynamics inherent to LLM inference: (1) *horizontal dynamics*, where token-level heterogeneity demands context-aware pruning decisions, and (2) *vertical dynamics*, where the distinct functional roles of MLP and self-attention layers necessitate component-specific pruning policies. We introduce **SkipGPT**, a dynamic layer pruning framework designed to optimize computational resource allocation through two core innovations: (1) global token-aware routing to prioritize critical tokens and (2) decoupled pruning policies for MLP and self-attention components. To mitigate training instability, we propose a two-stage optimization paradigm: first, a disentangled training phase that learns routing strategies via soft parameterization to avoid premature pruning decisions, followed by parameter-efficient LoRA fine-tuning to restore performance impacted by layer removal. Extensive experiments demonstrate that SkipGPT reduces over 40% model parameters while matching or exceeding the performance of the original dense model across benchmarks. By harmonizing dynamic efficiency with preserved expressivity, SkipGPT advances the practical deployment of scalable, resource-aware LLMs. Our code is publicly available at: https://github.com/EIT-NLP/SkipGPT.
|
compression, pruning, layer skipping, efficiency, large language models, model optimization
|
We propose SkipGPT, a dynamic pruning framework that adapts to token complexity, decouples MLP and attention, and uses a two-stage training paradigm, achieving 40% fewer parameters while preserving or improving performance.
| 11,885 | null |
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] |
https://github.com/EIT-NLP/SkipGPT
|
773 |
Reflect-then-Plan: Offline Model-Based Planning through a Doubly Bayesian Lens
|
https://openreview.net/forum?id=pQYEMwHd6c
|
[
"Jihwan Jeong",
"Xiaoyu Wang",
"Jingmin Wang",
"Scott Sanner",
"Pascal Poupart"
] |
Poster
|
Offline reinforcement learning (RL) is crucial when online exploration is costly or unsafe but often struggles with high epistemic uncertainty due to limited data. Existing methods rely on fixed conservative policies, restricting adaptivity and generalization. To address this, we propose Reflect-then-Plan (RefPlan), a novel _doubly Bayesian_ offline model-based (MB) planning approach. RefPlan unifies uncertainty modeling and MB planning by recasting planning as Bayesian posterior estimation. At deployment, it updates a belief over environment dynamics using real-time observations, incorporating uncertainty into MB planning via marginalization. Empirical results on standard benchmarks show that RefPlan significantly improves the performance of conservative offline RL policies. In particular, RefPlan maintains robust performance under high epistemic uncertainty and limited data, while demonstrating resilience to changing environment dynamics, improving the flexibility, generalizability, and robustness of offline-learned policies.
|
Offline reinforcement learning, Model-based planning, Bayesian inference, Bayesian reinforcement learning
| null | 11,881 | null |
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] |
https://github.com/jihwan-jeong/offline-rl
|
774 |
LEVIS: Large Exact Verifiable Input Spaces for Neural Networks
|
https://openreview.net/forum?id=WFIMSlNS7C
|
[
"Mohamad Fares El Hajj Chehade",
"WENTING LI",
"Brian Wesley Bell",
"Russell Bent",
"Saif R. Kazi",
"Hao Zhu"
] |
Poster
|
The robustness of neural networks is crucial in safety-critical applications, where identifying a reliable input space is essential for effective model selection, robustness evaluation, and the development of reliable control strategies. Most existing robustness verification methods assess the worst-case output under the assumption that the input space is known. However, precisely identifying a verifiable input space $ \mathcal{C} $, where no adversarial examples exist, is challenging due to the possible high dimensionality, discontinuity, and non-convex nature of the input space. To address this challenge, we propose a novel framework, **LEVIS**, comprising **LEVIS-$\alpha$** and **LEVIS-$\beta$**. **LEVIS-$\alpha$** identifies a single, large verifiable ball that intersects at least two boundaries of a bounded region $ \mathcal{C} $, while **LEVIS-$\beta$** systematically captures the entirety of the verifiable space by integrating multiple verifiable balls. Our contributions are fourfold: we introduce a verification framework, **LEVIS**, incorporating two optimization techniques for computing nearest and directional adversarial points based on mixed-integer programming (MIP); to enhance scalability, we integrate complementary constrained (CC) optimization with a reduced MIP formulation, achieving up to a 17-fold reduction in runtime by approximating the verifiable region in a principled way; we provide a theoretical analysis characterizing the properties of the verifiable balls obtained through **LEVIS-$\alpha$**; and we validate our approach across diverse applications, including electrical power flow regression and image classification, demonstrating performance improvements and visualizing the geometric properties of the verifiable region.
|
robustness of neural networks, verifiable input space, adversarial examples, mixed-integer programming
|
We propose LEVIS, a set of algorithms to locate a large verifiable input space for neural networks.
| 11,872 |
2408.08824
|
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] |
https://github.com/LEVIS-LANL/LEVIS
|
775 |
Active feature acquisition via explainability-driven ranking
|
https://openreview.net/forum?id=J8YRdm39jn
|
[
"Osman Berke Guney",
"Ketan Suhaas Saichandran",
"Karim Elzokm",
"Ziming Zhang",
"Vijaya B Kolachalama"
] |
Poster
|
In many practical applications, including medicine, acquiring all relevant data for machine learning models is often infeasible due to constraints on time, cost, and resources. This makes it important to selectively acquire only the most informative features, yet traditional static feature selection methods fall short in scenarios where feature importance varies across instances. Here, we propose an active feature acquisition (AFA) framework, which dynamically selects features based on their importance to each individual case. Our method leverages local explanation techniques to generate instance-specific feature importance rankings. We then reframe the AFA problem as a feature prediction task, introducing a policy network grounded in a decision transformer architecture. This policy network is trained to select the next most informative feature by learning from the feature importance rankings. As a result, features are acquired sequentially, ordered by their predictive significance, leading to more efficient feature selection and acquisition. Extensive experiments on multiple datasets demonstrate that our approach outperforms current state-of-the-art AFA methods in both predictive accuracy and feature acquisition efficiency. These findings highlight the promise of an explainability-driven AFA strategy in scenarios where the cost of feature acquisition is a key concern.
|
Active feature acquisition, Policy network, Feature importance
|
An explainability-driven AFA method uses local explanations and a decision-transformer policy network to dynamically select the most relevant features, achieving better accuracy and efficiency than existing methods.
| 11,870 | null |
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] |
https://github.com/vkola-lab/icml2025
|
776 |
What makes an Ensemble (Un) Interpretable?
|
https://openreview.net/forum?id=kR5ZAP7F9b
|
[
"Shahaf Bassan",
"Guy Amir",
"Meirav Zehavi",
"Guy Katz"
] |
Poster
|
Ensemble models are widely recognized in the ML community for their limited interpretability. For instance, while a single decision tree is considered interpretable, ensembles of trees (e.g., boosted trees) are often treated as black-boxes. Despite this folklore recognition, there remains a lack of rigorous mathematical understanding of what particularly makes an ensemble (un)-interpretable, including how fundamental factors like the (1) *number*, (2) *size*, and (3) *type* of base models influence its interpretability. In this work, we seek to bridge this gap by applying concepts from computational complexity theory to study the challenges of generating explanations for various ensemble configurations. Our analysis uncovers nuanced complexity patterns influenced by various factors. For example, we demonstrate that under standard complexity assumptions like P$\neq$NP, interpreting ensembles remains intractable even when base models are of constant size. Surprisingly, the complexity changes drastically with the number of base models: small ensembles of decision trees are efficiently interpretable, whereas ensembles of linear models remain intractable, even with a constant number of models. We believe that our findings provide a more robust foundation for understanding the interpretability of ensembles, emphasizing the benefits of examining it through a computational complexity lens.
|
interpretability, explainable AI
| null | 11,869 |
2506.08216
|
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] | null |
777 |
Theoretical Performance Guarantees for Partial Domain Adaptation via Partial Optimal Transport
|
https://openreview.net/forum?id=YF17x9e3J2
|
[
"Jayadev Naram",
"Fredrik Hellström",
"Ziming Wang",
"Rebecka Jörnsten",
"Giuseppe Durisi"
] |
Poster
|
In many scenarios of practical interest, labeled data from a target distribution are scarce while labeled data from a related source distribution are abundant. One particular setting of interest arises when the target label space is a subset of the source label space, leading to the framework of partial domain adaptation (PDA). Typical approaches to PDA involve minimizing a domain alignment term and a weighted empirical loss on the source data, with the aim of transferring knowledge between domains. However, a theoretical basis for this procedure is lacking, and in particular, most existing weighting schemes are heuristic. In this work, we derive generalization bounds for the PDA problem based on partial optimal transport. These bounds corroborate the use of the partial Wasserstein distance as a domain alignment term, and lead to theoretically motivated explicit expressions for the empirical source loss weights. Inspired by these bounds, we devise a practical algorithm for PDA, termed WARMPOT. Through extensive numerical experiments, we show that WARMPOT is competitive with recent approaches, and that our proposed weights improve on existing schemes.
|
Partial Domain Adaptation, Optimal Transport, Generalization Bounds
| null | 11,867 |
2506.02712
|
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] |
https://github.com/JayD2106/WARMPOT
|
778 |
The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret
|
https://openreview.net/forum?id=SkYBAXPUBw
|
[
"Lukas Fluri",
"Leon Lang",
"Alessandro Abate",
"Patrick Forré",
"David Krueger",
"Joar Max Viktor Skalse"
] |
Poster
|
In reinforcement learning, specifying reward functions that capture the intended task can be very challenging. Reward learning aims to address this issue by *learning* the reward function. However, a learned reward model may have a low error on the data distribution, and yet subsequently produce a policy with large regret. We say that such a reward model has an *error-regret mismatch*. The main source of an error-regret mismatch is the distributional shift that commonly occurs during policy optimization. In this paper, we mathematically show that a sufficiently low expected test error of the reward model guarantees low worst-case regret, but that for any *fixed* expected test error, there exist realistic data distributions that allow for error-regret mismatch to occur. We then show that similar problems persist even when using policy regularization techniques, commonly employed in methods such as RLHF. We hope our results stimulate the theoretical and empirical study of improved methods to learn reward models, and better ways to measure their quality reliably.
|
Reward learning, RLHF, RL, Safety, Distributional shift, Generalization, Learning Theory
|
We study the relationship between the expected error of a reward model on some data distribution and the the reget of policies trained using the reward model.
| 11,860 |
2406.15753
|
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] | null |
779 |
Volume Optimality in Conformal Prediction with Structured Prediction Sets
|
https://openreview.net/forum?id=oNDhnGrD51
|
[
"Chao Gao",
"Liren Shan",
"Vaidehi Srinivas",
"Aravindan Vijayaraghavan"
] |
Poster
|
Conformal Prediction is a widely studied technique to construct prediction sets of future observations. Most conformal prediction methods focus on achieving the necessary coverage guarantees, but do not provide formal guarantees on the size (volume) of the prediction sets. We first prove the impossibility of volume optimality where any distribution-free method can only find a trivial solution. We then introduce a new notion of volume optimality by restricting the prediction sets to belong to a set family (of finite VC-dimension), specifically a union of $k$-intervals. Our main contribution is an efficient distribution-free algorithm based on dynamic programming (DP) to find a union of $k$-intervals that is guaranteed for any distribution to have near-optimal volume among all unions of $k$-intervals satisfying the desired coverage property.
By adopting the framework of distributional conformal prediction (Chernozhukov et al., 2021), the new DP based conformity score can also be applied to achieve approximate conditional coverage and conditional restricted volume optimality, as long as a reasonable estimator of the conditional CDF is available.
While the theoretical results already establish volume-optimality guarantees, they are complemented by experiments that demonstrate that our method can significantly outperform existing methods in many settings.
|
Conformal Prediction, Volume Optimality, Structured Prediction Sets
|
This work provides conformal prediction methods with restricted volume optimality in both the unsupervised setting and the supervised setting.
| 11,856 |
2502.16658
|
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] | null |
780 |
System-Aware Unlearning Algorithms: Use Lesser, Forget Faster
|
https://openreview.net/forum?id=DnuuFjCsDo
|
[
"Linda Lu",
"Ayush Sekhari",
"Karthik Sridharan"
] |
Poster
|
Machine unlearning addresses the problem of updating a machine learning model/system trained on a dataset $S$ so that the influence of a set of deletion requests $U \subseteq S$ on the unlearned model is minimized. The gold standard definition of unlearning demands that the updated model, after deletion, be nearly identical to the model obtained by retraining. This definition is designed for a worst-case attacker (one who can recover not only the unlearned model but also the remaining data samples, i.e., $S \setminus U$). Such a stringent definition has made developing efficient unlearning algorithms challenging. However, such strong attackers are also unrealistic. In this work, we propose a new definition, *system-aware unlearning*, which aims to provide unlearning guarantees against an attacker that can at best only gain access to the data stored in the system for learning/unlearning requests and not all of $S\setminus U$. With this new definition, we use the simple intuition that if a system can store less to make its learning/unlearning updates, it can be more secure and update more efficiently against a system-aware attacker. Towards that end, we present an exact system-aware unlearning algorithm for linear classification using a selective sampling-based approach, and we generalize the method for classification with general function classes. We theoretically analyze the tradeoffs between deletion capacity, accuracy, memory, and computation time.
|
machine unlearning, sample compression, selective sampling, learning theory
|
We present a new definition of unlearning, system aware unlearning, and we demonstrate that we can design more efficient unlearning algorithms under this definition.
| 11,855 |
2506.06073
|
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] | null |
781 |
Iterative Vectors: In-Context Gradient Steering without Backpropagation
|
https://openreview.net/forum?id=1v3XEcRMyP
|
[
"Yiting Liu",
"Zhi-Hong Deng"
] |
Poster
|
In-context learning has become a standard approach for utilizing language models.
However, selecting and processing suitable demonstration examples can be challenging and time-consuming, especially when dealing with large numbers of them.
We propose Iterative Vectors (IVs), a technique that explores activation space to enhance in-context performance by simulating gradient updates during inference.
IVs extract and iteratively refine activation-based meta-gradients, applying them during inference without requiring backpropagation at any stage.
We evaluate IVs across various tasks using four popular models and observe significant improvements.
Our findings suggest that in-context activation steering is a promising direction, opening new avenues for future research.
|
In-Context Learning, Activation Steering, Backpropagation-Free
|
Iterative Vectors enhance in-context learning by editing activations in language models with simulated gradients, demonstrating significant improvements across tasks without backpropagation.
| 11,849 | null |
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] |
https://github.com/ArkciaTheDragon/iterative-vectors
|
782 |
Stochastic Encodings for Active Feature Acquisition
|
https://openreview.net/forum?id=MjVmVakGdx
|
[
"Alexander Luke Ian Norcliffe",
"Changhee Lee",
"Fergus Imrie",
"Mihaela van der Schaar",
"Pietro Lio"
] |
Poster
|
Active Feature Acquisition is an instance-wise, sequential decision making problem. The aim is to dynamically select which feature to measure based on current observations, independently for each test instance. Common approaches either use Reinforcement Learning, which experiences training difficulties, or greedily maximize the conditional mutual information of the label and unobserved features, which makes myopic acquisitions. To address these shortcomings, we introduce a latent variable model, trained in a supervised manner. Acquisitions are made by reasoning about the features across many possible unobserved realizations in a stochastic latent space. Extensive evaluation on a large range of synthetic and real datasets demonstrates that our approach reliably outperforms a diverse set of baselines.
|
Active Feature Acquisition, Dynamic Feature Selection
|
We present a new method using stochastic encoders for Active Feature Acquisition, the test time task of iteratively observing features to improve the current prediction.
| 11,841 | null |
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] |
https://github.com/a-norcliffe/SEFA
|
783 |
From Logits to Hierarchies: Hierarchical Clustering made Simple
|
https://openreview.net/forum?id=t0x2VnBskT
|
[
"Emanuele Palumbo",
"Moritz Vandenhirtz",
"Alain Ryser",
"Imant Daunhawer",
"Julia E Vogt"
] |
Poster
|
The hierarchical structure inherent in many real-world datasets makes the modeling of such hierarchies a crucial objective in both unsupervised and supervised machine learning. While recent advancements have introduced deep architectures specifically designed for hierarchical clustering, we adopt a critical perspective on this line of research. Our findings reveal that these methods face significant limitations in scalability and performance when applied to realistic datasets.~Given these findings, we present an alternative approach and introduce a lightweight method that builds on pre-trained non-hierarchical clustering models. Remarkably, our approach outperforms specialized deep models for hierarchical clustering, and it is broadly applicable to any pre-trained clustering model that outputs logits, without requiring any fine-tuning. To highlight the generality of our approach, we extend its application to a supervised setting, demonstrating its ability to recover meaningful hierarchies from a pre-trained ImageNet classifier. Our results establish a practical and effective alternative to existing deep hierarchical clustering methods, with significant advantages in efficiency, scalability and performance.
|
Hierarchical Clustering, Clustering, Interpretability, Unsupervised Learning
| null | 11,839 |
2410.07858
|
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] | null |
784 |
Aligning Multimodal Representations through an Information Bottleneck
|
https://openreview.net/forum?id=zltxOTEtfm
|
[
"Antonio Almudévar",
"José Miguel Hernández-Lobato",
"Sameer Khurana",
"Ricard Marxer",
"Alfonso Ortega"
] |
Poster
|
Contrastive losses have been extensively used as a tool for multimodal representation learning.
However, it has been empirically observed that their use is not effective to learn an aligned representation space.
In this paper, we argue that this phenomenon is caused by the presence of modality-specific information in the representation space.
Although some of the most widely used contrastive losses maximize the mutual information between representations of both modalities, they are not designed to remove the modality-specific information.
We give a theoretical description of this problem through the lens of the Information Bottleneck Principle.
We also empirically analyze how different hyperparameters affect the emergence of this phenomenon in a controlled experimental setup.
Finally, we propose a regularization term in the loss function that is derived by means of a variational approximation and aims to increase the representational alignment.
We analyze in a set of controlled experiments and real-world applications the advantages of including this regularization term.
|
multimodal representation learning, representational alignment, modality gap, information bottleneck
|
Contrastive losses maximize shared information but retain modality-specific content, causing misalignment. We formalize this with information theory and propose a lightweight regularizer to improve cross-modal representation alignment.
| 11,836 |
2506.04870
|
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] |
https://github.com/antonioalmudevar/multimodal_ib
|
785 |
Metadata Conditioning Accelerates Language Model Pre-training
|
https://openreview.net/forum?id=DdMMzlI5YE
|
[
"Tianyu Gao",
"Alexander Wettig",
"Luxi He",
"Yihe Dong",
"Sadhika Malladi",
"Danqi Chen"
] |
Poster
|
The vast diversity of styles, domains, and quality levels present in language model pre-training corpora is essential in developing general model capabilities, but efficiently learning and deploying the correct behaviors exemplified in each of these heterogeneous data sources is challenging. To address this, we propose a new method, termed Metadata Conditioning then Cooldown (MeCo), to incorporate additional learning cues during pre-training. MeCo first provides metadata (e.g., URLs like en.wikipedia.org) alongside the text during training and later uses a cooldown phase with only the standard text, thereby enabling the model to function normally even without metadata. MeCo significantly accelerates pre-training across different model scales (600M to 8B parameters) and training sources (C4, RefinedWeb, and DCLM). For instance, a 1.6B language model trained with MeCo matches the downstream task performance of standard pre-training while using 33% less data. Additionally, MeCo enables us to steer language models by conditioning the inference prompt on either real or fabricated metadata that encodes the desired properties of the output: for example, prepending wikipedia.org to reduce harmful generations or factquizmaster.com (fabricated) to improve common knowledge task performance. We also demonstrate that MeCo is compatible with different types of metadata, such as model-generated topics. MeCo is remarkably simple, adds no computational overhead, and demonstrates promise in producing more capable and steerable language models.
|
language models, pre-training, metadata conditioning, acceleration, URLs
|
We propose to prepend metadata information (such as URLs) to pre-training documents. Our method, metadata conditioning then cooldown (MeCo), significantly accelerates pre-training.
| 11,833 |
2501.01956
|
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] |
https://github.com/princeton-pli/MeCo
|
786 |
Online Sparsification of Bipartite-Like Clusters in Graphs
|
https://openreview.net/forum?id=j68NJfjMVH
|
[
"Joyentanuj Das",
"Suranjan De",
"He Sun"
] |
Poster
|
Graph clustering is an important algorithmic technique for analysing massive graphs, and has been widely applied in many research fields of data science. While the objective of most graph clustering algorithms is to find a vertex set of low conductance, there has been a sequence of recent studies that highlight the importance of the inter-connection between clusters when analysing real-world datasets. Following this line of research, in this work we study bipartite-like clusters and present efficient and online algorithms that find such clusters in both undirected graphs and directed ones. We conduct experimental studies on both synthetic and real-world datasets, and show that our algorithms significantly speedup the running time of existing clustering algorithms while preserving their effectiveness.
|
bipartite-like components, sparsification, online algorithms
|
We study densely connected clusters in graphs and introduce two sparsification algorithms that preserve the structure of these clusters in both undirected graphs and directed ones.
| 11,820 | null |
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] | null |
787 |
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
|
https://openreview.net/forum?id=C7dmhyTDrx
|
[
"Yuecheng Li",
"Lele Fu",
"Tong Wang",
"Jian Lou",
"Bin Chen",
"Lei Yang",
"Jian Shen",
"Zibin Zheng",
"Chuan Chen"
] |
Poster
|
To defend against privacy leakage of user data, differential privacy is widely used in federated learning, but it is not free. The addition of noise randomly disrupts the semantic integrity of the model and this disturbance accumulates with increased communication rounds. In this paper, we introduce a novel federated learning framework with rigorous privacy guarantees, named **FedCEO**, designed to strike a trade-off between model utility and user privacy by letting clients "***C**ollaborate with **E**ach **O**ther*". Specifically, we perform efficient tensor low-rank proximal optimization on stacked local model parameters at the server, demonstrating its capability to ***flexibly*** truncate high-frequency components in spectral space. This capability implies that our FedCEO can effectively recover the disrupted semantic information by smoothing the global semantic space for different privacy settings and continuous training processes. Moreover, we improve the SOTA utility-privacy trade-off bound by order of $\sqrt{d}$, where $d$ is the input dimension. We illustrate our theoretical results with experiments on representative datasets and observe significant performance improvements and strict privacy guarantees under different privacy settings. The **code** is available at https://github.com/6lyc/FedCEO_Collaborate-with-Each-Other.
|
Federated Learning, Differential Privacy, Utility-Privacy Trade-off
|
In this paper, we formulate a novel federated learning framework that achieves a guaranteed and high-quality trade-off between model utility and user privacy.
| 11,816 |
2402.07002
|
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] |
https://github.com/6lyc/FedCEO_Collaborate-with-Each-Other
|
788 |
The Devil Is in the Details: Tackling Unimodal Spurious Correlations for Generalizable Multimodal Reward Models
|
https://openreview.net/forum?id=b0qRSUcQP7
|
[
"Zichao Li",
"Xueru Wen",
"Jie Lou",
"Yuqiu Ji",
"Yaojie Lu",
"Xianpei Han",
"Debing Zhang",
"Le Sun"
] |
Poster
|
Multimodal Reward Models (MM-RMs) are crucial for aligning Large Language Models (LLMs) with human preferences, particularly as LLMs increasingly interact with multimodal data. However, we find that MM-RMs trained on existing datasets often struggle to generalize to out-of-distribution data due to their reliance on unimodal spurious correlations, primarily text-only shortcuts within the training distribution, which prevents them from leveraging true multimodal reward functions. To address this, we introduce a Shortcut-aware MM-RM learning algorithm that mitigates this issue by dynamically reweighting training samples, shifting the distribution toward better multimodal understanding, and reducing dependence on unimodal spurious correlations. Our experiments demonstrate significant improvements in generalization, downstream task performance, and scalability, establishing a more robust framework for multimodal reward modeling. Our source code is provided on https://github.com/alignrm/Generalizable-MM-RM.
|
Multimodal Reward Model, Generalization, Spurious Correlations
| null | 11,808 |
2503.03122
|
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] |
https://github.com/alignrm/Generalizable-MM-RM
|
789 |
Upcycling Text-to-Image Diffusion Models for Multi-Task Capabilities
|
https://openreview.net/forum?id=GfWucMJt1S
|
[
"Ruchika Chavhan",
"Abhinav Mehrotra",
"Malcolm Chadwick",
"Alberto Gil Couto Pimentel Ramos",
"Luca Morreale",
"Mehdi Noroozi",
"Sourav Bhattacharya"
] |
Poster
|
Text-to-image synthesis has witnessed remarkable advancements in recent years. Many attempts have been made to adopt text-to-image models to support multiple tasks. However, existing approaches typically require resource-intensive re-training or additional parameters to accommodate for the new tasks, which makes the model inefficient for on-device deployment. We propose *Multi-Task Upcycling* (MTU), a simple yet effective recipe that extends the capabilities of a pre-trained text-to-image diffusion model to support a variety of image-to-image generation tasks. MTU replaces Feed-Forward Network (FFN) layers in the diffusion model with smaller FFNs, referred to as *experts*, and combines them with a dynamic routing mechanism. To the best of our knowledge, MTU is the first multi-task diffusion modeling approach that seamlessly blends multi-tasking with on-device compatibility, by mitigating the issue of parameter inflation. We show that the performance of MTU is on par with the single-task fine-tuned diffusion models across several tasks including *image editing, super-resolution*, and *inpainting*, while maintaining similar latency and computational load (GFLOPs) as the single-task fine-tuned models.
|
multi-task learning, upcycling, diffusion models
|
Multi-task Learning in Diffusion Models
| 11,801 |
2503.11905
|
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] | null |
790 |
Provably Cost-Sensitive Adversarial Defense via Randomized Smoothing
|
https://openreview.net/forum?id=D7qKAO34tp
|
[
"Yuan Xin",
"Dingfan Chen",
"Michael Backes",
"Xiao Zhang"
] |
Poster
|
As machine learning models are deployed in critical applications, robustness against adversarial perturbations is crucial. While numerous defensive algorithms have been proposed to counter such attacks, they typically assume that all adversarial transformations are equally important, an assumption that rarely aligns with real-world applications. To address this, we study the problem of robust learning against adversarial perturbations under cost-sensitive scenarios, where the potential harm of different types of misclassifications is encoded in a cost matrix. Our solution introduces a provably robust learning algorithm to certify and optimize for cost-sensitive robustness, building on the scalable certification framework of randomized smoothing. Specifically, we formalize the definition of cost-sensitive certified radius and propose our novel adaptation of the standard certification algorithm to generate tight robustness certificates tailored to any cost matrix. In addition, we design a robust training method that improves certified cost-sensitive robustness without compromising model accuracy. Extensive experiments on benchmark datasets, including challenging ones unsolvable by existing methods, demonstrate the effectiveness of our certification algorithm and training method across various cost-sensitive scenarios.
|
Certification, cost-sensitive learning
| null | 11,792 |
2310.08732
|
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0.012556541711091995,
0.0379093661904335,
0.013865439221262932,
0.0008370198775082827
] |
https://github.com/trustmlrg/cs-rs
|
791 |
Protein Structure Tokenization: Benchmarking and New Recipe
|
https://openreview.net/forum?id=o4ANDWaomX
|
[
"Xinyu Yuan",
"Zichen Wang",
"Marcus D. Collins",
"Huzefa Rangwala"
] |
Poster
|
Recent years have witnessed a surge in the development of protein structural tokenization methods, which chunk protein 3D structures into discrete or continuous representations. Structure tokenization enables the direct application of powerful techniques like language modeling for protein structures, and large multimodal models to integrate structures with protein sequences and functional texts. Despite the progress, the capabilities and limitations of these methods remain poorly understood due to the lack of a unified evaluation framework. We first introduce **StructTokenBench**, a framework that comprehensively evaluates the quality and efficiency of structure tokenizers, focusing on fine-grained local substructures rather than global structures, as typical in existing benchmarks. Our evaluations reveal that no single model dominates all benchmarking perspectives. Observations of codebook under-utilization led us to develop **AminoAseed**, a simple yet effective strategy that enhances codebook gradient updates and optimally balances codebook size and dimension for improved tokenizer utilization and quality. Compared to the leading model ESM3, our method achieves an average of 6.31\% performance improvement across 24 supervised tasks, with sensitivity and utilization rates increased by 12.83\% and 124.03\%, respectively. Source code and model weights are available at https://github.com/KatarinaYuan/StructTokenBench.
|
protein structure tokenization, benchmarking, VQ-VAE
|
A comprehensive benchmark for protein structure tokenization methods, focusing on fine-grained local protein structure representation, with a new tokenizer recipe showing enhanced performance
| 11,790 |
2503.00089
|
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] |
https://github.com/KatarinaYuan/StructTokenBench
|
792 |
LAuReL: Learned Augmented Residual Layer
|
https://openreview.net/forum?id=rUDRWP9WvZ
|
[
"Gaurav Menghani",
"Ravi Kumar",
"Sanjiv Kumar"
] |
Poster
|
One of the core pillars of efficient deep learning methods are architectural improvements, such as residual/skip connections, which have led to significantly better model convergence and quality. Since their introduction, residual connections have become ubiquitous not only in convolutional neural networks but also in transformer-based architectures, the backbone of LLMs.
In this paper, we introduce the Learned Augmented Residual Layer (LAuReL) --- a novel generalization of the canonical residual connection --- designed to serve as an in-situ replacement while outperforming it in both model quality and footprint metrics. Our experiments show that LAuReL can enhance quality for both vision and language models while adding fewer parameters and incurring less latency and memory overhead than naively increasing parameter count.
For example, on the ImageNet-1K task, LAuReL achieves the same model quality improvements as naively adding an extra layer while using $2.6 \times$ fewer parameters. Similarly, when pre-training 1B and 4B parameter LLMs, LAuReL improves performance on a variety of challenging downstream evaluation tasks by 2.54\% to 20.05\%, while adding only 0.012\% and 0.1\% additional parameters, respectively.
|
residual connection, skip connection, residual layer, resnet, residual
|
We introduce LAuReL, which is a novel replacement for the residual / skip connection. LAuReL significantly improves performance of vision and language models while adding as few as 0.01% more parameters and negligible extra latency.
| 11,789 |
2411.07501
|
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] | null |
793 |
Towards Theoretical Understanding of Sequential Decision Making with Preference Feedback
|
https://openreview.net/forum?id=SqnViOBHP0
|
[
"Simone Drago",
"Marco Mussi",
"Alberto Maria Metelli"
] |
Poster
|
The success of sequential decision-making approaches, such as *reinforcement learning* (RL), is closely tied to the availability of a reward feedback. However, designing a reward function that encodes the desired objective is a challenging task. In this work, we address a more realistic scenario: sequential decision making with preference feedback provided, for instance, by a human expert. We aim to build a theoretical basis linking *preferences*, (non-Markovian) *utilities*, and (Markovian) *rewards*, and we study the connections between them. First, we model preference feedback using a partial (pre)order over trajectories, enabling the presence of incomparabilities that are common when preferences are provided by humans but are surprisingly overlooked in existing works. Second, to provide a theoretical justification for a common practice, we investigate how a preference relation can be approximated by a multi-objective utility. We introduce a notion of preference-utility compatibility and analyze the computational complexity of this transformation, showing that constructing the minimum-dimensional utility is NP-hard. Third, we propose a novel concept of preference-based policy dominance that does not rely on utilities or rewards and discuss the computational complexity of assessing it. Fourth, we develop a computationally efficient algorithm to approximate a utility using (Markovian) rewards and quantify the error in terms of the suboptimality of the optimal policy induced by the approximating reward. This work aims to lay the foundation for a principled approach to sequential decision making from preference feedback, with promising potential applications in RL from human feedback.
|
preference-based, sequential decision making, preference feedback
| null | 11,786 | null |
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] | null |
794 |
SquareχPO: Differentially Private and Robust χ2-Preference Optimization in Offline Direct Alignment
|
https://openreview.net/forum?id=eWQQPtfJjV
|
[
"Xingyu Zhou",
"Yulian Wu",
"Wenqian Weng",
"Francesco Orabona"
] |
Poster
|
In this paper, we theoretically study the offline alignment of language models with human preference feedback, under both preference label corruption and privacy protections. To this end, we propose a variant of \texttt{$\chi$PO} -- \texttt{Square}\texttt{$\chi$PO}, which is a simple one-line change of \texttt{$\chi$PO} with the standard log-loss replaced by a new square loss over probability. Thanks to the inherent nice properties of this new loss, we have advanced the state-of-the-art of differentially private and robust alignment. Specifically, for the local model of label privacy, \texttt{Square}\texttt{$\chi$PO} is the first one that attains optimal rate based on single-policy concentrability even with general function approximations. It also gives the first result under the central model of privacy protection over both prompts (responses) and labels. On the robustness side against Huber label corruption, \texttt{Square}\texttt{$\chi$PO} is the first alignment method that has a meaningful theoretical guarantee under general function approximations. More importantly, \texttt{Square}\texttt{$\chi$PO} can address privacy protection and corruption \emph{simultaneously}, where an interesting separation is observed, implying that the order of privacy and corruption matters. Furthermore, we show that \texttt{Square}\texttt{$\chi$PO} can also be easily extended to handle the scenario of the general preference model with state-of-the-art guarantees under corruption and privacy. Last but not least, all of our theoretical guarantees enjoy a unified analysis, building upon a new result on the generalization error bounds of least-square regression under corruption and privacy constraints, which we believe is of independent interest to the community.
|
Offline alignment, general function approximations, differential privacy, robustness, single-policy concentrability
|
The new state-of-the-art theoretical guarantees in differentially private and robust offline alignments
| 11,783 | null |
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] | null |
795 |
The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models Via Visual Information Steering
|
https://openreview.net/forum?id=7BKcLeHQsm
|
[
"Zhuowei Li",
"Haizhou Shi",
"Yunhe Gao",
"Di Liu",
"Zhenting Wang",
"Yuxiao Chen",
"Ting Liu",
"Long Zhao",
"Hao Wang",
"Dimitris N. Metaxas"
] |
Poster
|
Large Vision-Language Models (LVLMs) can reason effectively over both textual and visual inputs, but they tend to hallucinate syntactically coherent yet visually ungrounded contents. In this paper, we investigate the internal dynamics of hallucination by examining the tokens logits rankings throughout the generation process, revealing three key patterns in how LVLMs process information:
(1) *gradual visual information loss* -- visually grounded tokens gradually become less favored throughout generation, and
(2) *early excitation* -- semantically meaningful tokens achieve peak activation in the layers earlier than the final layer.
(3) *hidden genuine information* -- visually grounded tokens though not being eventually decided still retain relatively high rankings at inference.
Based on these insights, we propose **VISTA** (**V**isual **I**nformation **S**teering with **T**oken-logit **A**ugmentation), a training-free inference-time intervention framework that reduces hallucination while promoting genuine information. VISTA works by combining two complementary approaches: reinforcing visual information in activation space and leveraging early layer activations to promote semantically meaningful decoding.
Compared to existing methods, VISTA requires no external supervision and is applicable to various decoding strategies.
Extensive experiments show that VISTA on average reduces hallucination by about 40% on evaluated open-ended generation task, and it consistently outperforms existing methods on four benchmarks across four architectures under three decoding strategies. Code is available at https://github.com/LzVv123456/VISTA.
|
Large Vision-Language Model, Hallucination
| null | 11,781 |
2502.03628
|
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] |
https://github.com/LzVv123456/VISTA
|
796 |
MathConstruct: Challenging LLM Reasoning with Constructive Proofs
|
https://openreview.net/forum?id=focYle7CfD
|
[
"Mislav Balunovic",
"Jasper Dekoninck",
"Nikola Jovanović",
"Ivo Petrov",
"Martin Vechev"
] |
Poster
|
While Large Language Models (LLMs) demonstrate impressive performance in mathematics, existing math benchmarks come with significant limitations. Many focus on problems with fixed ground-truth answers, and are often saturated due to problem simplicity or the viability of guessing or memorization. Crucially, they capture only a narrow subset of relevant math problems. To address this research gap, we introduce MathConstruct, a new benchmark of 127 challenging problems sourced from various math competitions, which targets *constructive proofs*, a widely encountered problem type requiring the construction of mathematical objects with specific properties. These proofs are particularly suitable for LLM evaluation, as solution correctness can be easily verified. Our automated verifiers also enable MathConstruct to generate problem variations, used to evaluate robustness. State-of-the-art LLMs solve only 41\% of MathConstruct problems, highlighting its complexity and importance for LLM evaluation.
|
LLM, math, evaluation, benchmark, reasoning
|
A new benchmark of math olympiad problems for evaluating LLMs at constructive proofs
| 11,775 |
2502.10197
|
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] |
https://github.com/eth-sri/mathconstruct
|
797 |
The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence
|
https://openreview.net/forum?id=80IwJqlXs8
|
[
"Tom Wollschläger",
"Jannes Elstner",
"Simon Geisler",
"Vincent Cohen-Addad",
"Stephan Günnemann",
"Johannes Gasteiger"
] |
Poster
|
The safety alignment of large language models (LLMs) can be circumvented through adversarially crafted inputs, yet the mechanisms by which these attacks bypass safety barriers remain poorly understood. Prior work suggests that a *single* refusal direction in the model's activation space determines whether an LLM refuses a request. In this study, we propose a novel gradient-based approach to representation engineering and use it to identify refusal directions. Contrary to prior work, we uncover multiple independent directions and even multi-dimensional *concept cones* that mediate refusal. Moreover, we show that orthogonality alone does not imply independence under intervention, motivating the notion of *representational independence* that accounts for both linear and non-linear effects. Using this framework, we identify mechanistically independent refusal directions. We show that refusal mechanisms in LLMs are governed by complex spatial structures and identify functionally independent directions, confirming that multiple distinct mechanisms drive refusal behavior. Our gradient-based approach uncovers these mechanisms and can further serve as a foundation for future work on understanding LLMs.
|
Refusal directions, Large Language Model, Activation Steering, Interpretability, Representation Engineering, Probing, Causality, Interventions
|
We identify that refusal is mediated by polyhedral cones and analyze the interactions of their basis vectors with our novel notion of independence.
| 11,772 |
2502.17420
|
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] | null |
798 |
Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery
|
https://openreview.net/forum?id=GaCo82yC7z
|
[
"Paris Giampouras",
"HanQin Cai",
"Rene Vidal"
] |
Poster
|
In this paper, we focus on a matrix factorization-based approach for robust recovery of low-rank asymmetric matrices from corrupted measurements. We propose an Overparameterized Preconditioned Subgradient Algorithm (OPSA) and provide, for the first time in the literature, linear convergence rates independent of the rank of the sought asymmetric matrix in the presence of gross corruptions. Our work goes beyond existing results in preconditioned-type approaches addressing their current limitation, i.e., the lack of convergence guarantees in the case of asymmetric matrices of unknown rank. By applying our approach to (robust) matrix sensing, we highlight its merits when the measurement operator satisfies a mixed-norm restricted isometry property. Lastly, we present extensive numerical experiments that validate our theoretical results and demonstrate the effectiveness of our approach for different levels of overparameterization and corruption from outliers.
|
Matrix factorisation, low-rank, matrix sensing, preconditioned subgradient
|
Linear convergence rates of an overparameterized preconditioned subgradient algorithm for robust low-rank matrix estimation.
| 11,771 |
2410.16826
|
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] |
https://github.com/caesarcai/OPSA
|
799 |
MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking
|
https://openreview.net/forum?id=fyi34BxCwq
|
[
"Sebastian Farquhar",
"Vikrant Varma",
"David Lindner",
"David Elson",
"Caleb Biddulph",
"Ian Goodfellow",
"Rohin Shah"
] |
Poster
|
Future advanced AI systems may learn sophisticated strategies through reinforcement learning (RL) that humans cannot understand well enough to safely evaluate. We propose a training method which avoids agents learning undesired multi-step plans that receive high reward (multi-step "reward hacks") even if humans are not able to detect that the behavior is undesired. The method, Myopic Optimization with Non-myopic Approval (MONA), works by combining short-sighted optimization with far-sighted reward. We demonstrate that MONA can prevent multi-step reward hacking that ordinary RL causes, even without being able to detect the reward hacking and without any extra information that ordinary RL does not get access to. We study MONA empirically in three settings which model different misalignment failure modes including 2-step environments with LLMs representing delegated oversight and encoded reasoning and longer-horizon gridworld environments representing sensor tampering.
|
AI Alignment, Myopic Optimization, Reinforcement Learning, LLM Agents, Process Supervision
|
Preventing multi-step reward hacking in LLM agents by combining short-sighted optimization with far-sighted reward.
| 11,768 |
2501.13011
|
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] |
https://github.com/google-deepmind/mona
|
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