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500 |
Multiple-policy Evaluation via Density Estimation
|
https://openreview.net/forum?id=8nelB0aMry
|
[
"Yilei Chen",
"Aldo Pacchiano",
"Ioannis Paschalidis"
] |
Poster
|
We study the multiple-policy evaluation problem where we are given a set of $K$ policies and the goal is to evaluate their performance (expected total reward over a fixed horizon) to an accuracy $\epsilon$ with probability at least $1-\delta$. We propose an algorithm named CAESAR for this problem. Our approach is based on computing an approximately optimal sampling distribution and using the data sampled from it to perform the simultaneous estimation of the policy values. CAESAR has two phases. In the first phase, we produce coarse estimates of the visitation distributions of the target policies at a low order sample complexity rate that scales with $\tilde{O}(\frac{1}{\epsilon})$. In the second phase, we approximate the optimal sampling distribution and compute the importance weighting ratios for all target policies by minimizing a step-wise quadratic loss function inspired by the DualDICE objective. Up to low order and logarithmic terms CAESAR achieves a sample complexity $\tilde{O}\left(\frac{H^4}{\epsilon^2}\sum_{h=1}^H\max_{k\in[K]}\sum_{s,a}\frac{(d_h^{\pi^k}(s,a))^2}{\mu^*_h(s,a)}\right)$, where $d^{\pi}$ is the visitation distribution of policy $\pi$, $\mu^*$ is the optimal sampling distribution, and $H$ is the horizon.
|
Theoretical Reinforcement Learning; Policy Evaluation; Instance dependent results;
| null | 13,391 |
2404.00195
|
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] | null |
501 |
Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language Models
|
https://openreview.net/forum?id=y1SnRPDWx4
|
[
"Yukang Yang",
"Declan Iain Campbell",
"Kaixuan Huang",
"Mengdi Wang",
"Jonathan D. Cohen",
"Taylor Whittington Webb"
] |
Poster
|
Many recent studies have found evidence for emergent reasoning capabilities in large language models (LLMs), but debate persists concerning the robustness of these capabilities, and the extent to which they depend on structured reasoning mechanisms. To shed light on these issues, we study the internal mechanisms that support abstract reasoning in LLMs. We identify an emergent symbolic architecture that implements abstract reasoning via a series of three computations. In early layers, *symbol abstraction heads* convert input tokens to abstract variables based on the relations between those tokens. In intermediate layers, *symbolic induction heads* perform sequence induction over these abstract variables. Finally, in later layers, *retrieval heads* predict the next token by retrieving the value associated with the predicted abstract variable. These results point toward a resolution of the longstanding debate between symbolic and neural network approaches, suggesting that emergent reasoning in neural networks depends on the emergence of symbolic mechanisms.
|
Mechanistic interpretability, reasoning, abstraction, large language models
|
A three-stage architecture is identified that supports abstract reasoning in LLMs via a set of emergent symbol-processing mechanisms
| 13,386 |
2502.20332
|
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] |
https://github.com/yukang123/LLMSymbMech
|
502 |
Causal-PIK: Causality-based Physical Reasoning with a Physics-Informed Kernel
|
https://openreview.net/forum?id=QgSfbgzgbH
|
[
"Carlota Parés Morlans",
"Michelle Yi",
"Claire Chen",
"Sarah A Wu",
"Rika Antonova",
"Tobias Gerstenberg",
"Jeannette Bohg"
] |
Poster
|
Tasks that involve complex interactions between objects with unknown dynamics make planning before execution difficult. These tasks require agents to iteratively improve their actions after actively exploring causes and effects in the environment. For these type of tasks, we propose Causal-PIK, a method that leverages Bayesian optimization to reason about causal interactions via a Physics-Informed Kernel to help guide efficient search for the best next action. Experimental results on Virtual Tools and PHYRE physical reasoning benchmarks show that Causal-PIK outperforms state-of-the-art results, requiring fewer actions to reach the goal. We also compare Causal-PIK to human studies, including results from a new user study we conducted on the PHYRE benchmark. We find that Causal-PIK remains competitive on tasks that are very challenging, even for human problem-solvers.
|
active exploration, physical reasoning, causality, bayesian optimization
|
We propose a method that leverages Bayesian optimization to reason about causality via a Physics-Informed Kernel. This helps agents to efficiently find solutions to tasks that involve complex interactions between objects with unknown dynamics.
| 13,358 | null |
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] | null |
503 |
CoastalBench: A Decade-Long High-Resolution Dataset to Emulate Complex Coastal Processes
|
https://openreview.net/forum?id=h3EEUJaMLu
|
[
"Zelin Xu",
"Yupu Zhang",
"Tingsong Xiao",
"Maitane Olabarrieta Lizaso",
"Jose M. Gonzalez-Ondina",
"Zibo Liu",
"Shigang Chen",
"Zhe Jiang"
] |
Poster
|
Over 40\% of the global population lives within 100 kilometers of the coast, which contributes more than \$8 trillion annually to the global economy. Unfortunately, coastal ecosystems are increasingly vulnerable to more frequent and intense extreme weather events and rising sea levels. Coastal scientists use numerical models to simulate complex physical processes, but these models are often slow and expensive. In recent years, deep learning has become a promising alternative to reduce the cost of numerical models. However, progress has been hindered by the lack of a large-scale, high-resolution coastal simulation dataset to train and validate deep learning models. Existing studies often focus on relatively small datasets and simple processes. To fill this gap, we introduce a decade-long, high-resolution (<100m) coastal circulation modeling dataset on a real-world 3D mesh in southwest Florida with around 6 million cells. The dataset contains key oceanography variables (e.g., current velocities, free surface level, temperature, salinity) alongside external atmospheric and river forcings. We evaluated a customized Vision Transformer model that takes initial and boundary conditions and external forcings and predicts ocean variables at varying lead times. The dataset provides an opportunity to benchmark novel deep learning models for high-resolution coastal simulations (e.g., physics-informed machine learning, neural operator learning).
The code and dataset can be accessed at https://github.com/spatialdatasciencegroup/CoastalBench.
|
Coastal Processes, Dataset, Benchmark
| null | 13,347 | null |
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] |
https://github.com/spatialdatasciencegroup/CoastalBench
|
504 |
Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separation
|
https://openreview.net/forum?id=rm2WHra1fB
|
[
"Shyam Nuggehalli",
"Jifan Zhang",
"Lalit K Jain",
"Robert D Nowak"
] |
Poster
|
Class imbalance severely impacts machine learning performance on minority classes in real-world applications. While various solutions exist, active learning offers a fundamental fix by strategically collecting balanced, informative labeled examples from abundant unlabeled data. We introduce DIRECT, an algorithm that identifies class separation boundaries and selects the most uncertain nearby examples for annotation. By reducing the problem to one-dimensional active learning, DIRECT leverages established theory to handle batch labeling and label noise -- another common challenge in data annotation that particularly affects active learning methods.
Our work presents the first comprehensive study of active learning under both class imbalance and label noise. Extensive experiments on imbalanced datasets show DIRECT reduces annotation costs by over 60\% compared to state-of-the-art active learning methods and over 80\% versus random sampling, while maintaining robustness to label noise.
|
Deep Learning, Active Labeling, Class Imbalance
| null | 13,344 |
2312.09196
|
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] | null |
505 |
BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute
|
https://openreview.net/forum?id=tFBIbCVXkG
|
[
"Dujian Ding",
"Ankur Mallick",
"Shaokun Zhang",
"Chi Wang",
"Daniel Madrigal",
"Mirian Del Carmen Hipolito Garcia",
"Menglin Xia",
"Laks V. S. Lakshmanan",
"Qingyun Wu",
"Victor Rühle"
] |
Poster
|
Large language models (LLMs) are powerful tools but are often expensive to deploy at scale. LLM query routing mitigates this by dynamically assigning queries to models of varying cost and quality to obtain a desired tradeoff. Prior query routing approaches generate only one response from the selected model and a single response from a small (inexpensive) model was often not good enough to beat a response from a large (expensive) model due to which they end up overusing the large model and missing out on potential cost savings. However, it is well known that for small models, generating multiple responses and selecting the best can enhance quality while remaining cheaper than a single large-model response. We leverage this idea to propose BEST-Route, a novel routing framework that chooses a model and the number of responses to sample from it based on query difficulty and the quality thresholds. Experiments on real-world datasets demonstrate that our method reduces costs by up to 60% with less than 1% performance drop.
|
Large language models, Efficient ML, Query Routing, Test-time Optimal Compute
| null | 13,334 | null |
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] |
https://github.com/microsoft/best-route-llm
|
506 |
DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing
|
https://openreview.net/forum?id=Nv6mOSqUVA
|
[
"Alexander Bienstock",
"Ujjwal Kumar",
"Antigoni Polychroniadou"
] |
Poster
|
Federated Learning (FL) solutions with central Differential Privacy (DP) have seen large improvements in their utility in recent years arising from the matrix mechanism, while FL solutions with distributed (more private) DP have lagged behind. In this work, we introduce the distributed matrix mechanism to achieve the best-of-both-worlds; better privacy of distributed DP and better utility from the matrix mechanism. We accomplish this using a novel cryptographic protocol that securely transfers sensitive values across client committees
of different training iterations with constant communication overhead. This protocol accommodates the dynamic participation of users required by FL, including those that may drop out from the computation. We provide experiments which show that our mechanism indeed significantly improves the utility of FL models compared to previous distributed DP mechanisms, with little added overhead.
|
federated learning, differential privacy, matrix mechanism, cryptography
|
We achieve better privacy-utility tradeoff for Federated Learning with local differential privacy using the matrix mechanism and novel efficient cryptographic techniques.
| 13,332 |
2410.16161
|
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] | null |
507 |
Which Attention Heads Matter for In-Context Learning?
|
https://openreview.net/forum?id=C7XmEByCFv
|
[
"Kayo Yin",
"Jacob Steinhardt"
] |
Poster
|
Large language models (LLMs) exhibit impressive in-context learning (ICL) capability, enabling them to generate relevant responses from a handful of task demonstrations in the prompt.
Prior studies have suggested two different explanations for the mechanisms behind ICL:
induction heads that find and copy relevant tokens, and function vector (FV) heads whose activations compute a latent encoding of the ICL task.
To better understand which of the two distinct mechanisms drives ICL, we study and compare induction heads and FV heads in 12 language models. Through detailed ablations, we find that few-shot ICL is driven primarily by FV heads, especially in larger models. We also find that FV and induction heads are connected: many FV heads
start as induction heads during training before transitioning to the FV mechanism. This leads us to speculate that induction facilitates learning the more complex FV mechanism for ICL.
|
interpretability, in-context learning, large language models, mechanistic interpretability, induction heads
|
We compare the role of induction heads and function vector (FV) heads in in-context learning (ICL), we find that FV heads drive most of few-shot ICL which challenges the popular belief that induction implements ICL.
| 13,318 |
2502.14010
|
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] |
https://github.com/kayoyin/icl-heads
|
508 |
Byzantine-Resilient Federated Alternating Gradient Descent and Minimization for Partly-Decoupled Low Rank Matrix Learning
|
https://openreview.net/forum?id=iBOMvaa2aN
|
[
"Ankit Pratap Singh",
"Ahmed Ali Abbasi",
"Namrata Vaswani"
] |
Poster
|
This work has two contributions. First, we introduce a provably secure (Byzantine-resilient) sample- and communication-efficient alternating gradient descent (GD) and minimization based algorithms for solving the federated low rank matrix completion (LRMC) problem. This involves learning a low rank (LR) matrix from a small subset of its entries. Second, we extend our ideas to show how a special case of our algorithms also solves another partly-decoupled vertically federated LR matrix learning problem, that is LR column-wise sensing (LRCS), also referred to as multi-task linear representation learning in the literature. Finally, we also show how our results can be extended for the LR phase retrieval problem. In all problems, we consider column-wise or vertical federation, i.e. each node observes a small subset of entries of a disjoint column sub-matrix of the entire LR matrix. For the LRMC problem, horizontal federation is equivalent since it is symmetric across rows and columns; while for the other two it is not. In all problems, the data at different nodes is heterogeneous (not identically distributed), making it harder to obtain provable guarantees.
|
Byzantine robustness, Low Rank Matrix Recovery, Multi-Task Representation Learning
| null | 13,313 | null |
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] | null |
509 |
Constrained Pareto Set Identification with Bandit Feedback
|
https://openreview.net/forum?id=3BmllnhGpm
|
[
"Cyrille Kone",
"Emilie Kaufmann",
"Laura Richert"
] |
Poster
|
In this paper, we address the problem of identifying the Pareto Set under feasibility constraints in a multivariate bandit setting. Specifically, given a $K$-armed bandit with unknown means $\mu_1, \dots, \mu_K \in \mathbb{R}^d$, the goal is to identify the set of arms whose mean is not uniformly worse than that of another arm (i.e., not smaller for all objectives), while satisfying some known set of linear constraints, expressing, for example, some minimal performance on each objective. Our focus lies in fixed-confidence identification, for which we introduce an algorithm that significantly outperforms racing-like algorithms and the intuitive two-stage approach that first identifies feasible arms and then their Pareto Set. We further prove an information-theoretic lower bound on the sample complexity of any algorithm for constrained Pareto Set identification, showing that the sample complexity of our approach is near-optimal. Our theoretical results are supported by an extensive empirical evaluation on a series of benchmarks.
|
pure exploration, bandit, theoretical analysis, active learning, Pareto set identification, multi-objective bandits
|
We study the identification of the Pareto Set in multi-objective bandits under linear feasibility constraints.
| 13,304 |
2506.08127
|
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] | null |
510 |
A Theoretical Framework For Overfitting In Energy-based Modeling
|
https://openreview.net/forum?id=SDC3D4vdpb
|
[
"Giovanni Catania",
"Aurélien Decelle",
"Cyril Furtlehner",
"Beatriz Seoane"
] |
Poster
|
We investigate the impact of limited data on training pairwise energy-based models for inverse problems aimed at identifying interaction networks. Utilizing the Gaussian model as testbed, we dissect training trajectories across the eigenbasis of the coupling matrix, exploiting the independent evolution of eigenmodes and revealing that the learning timescales are tied to the spectral decomposition of the empirical covariance matrix. We see that optimal points for early stopping arise from the interplay between these timescales and the initial conditions of training. Moreover, we show that finite data corrections can be accurately modeled through asymptotic random matrix theory calculations and provide the counterpart of generalized cross-validation in the energy based model context. Our analytical framework extends to binary-variable maximum-entropy pairwise models with minimal variations.
These findings offer strategies to control overfitting in discrete-variable models through empirical shrinkage corrections, improving the management of overfitting in energy-based generative models.
Finally, we propose a generalization to arbitrary energy-based models by deriving the neural tangent kernel dynamics of the score function under the score-matching algorithm.
|
energy-based models, overfitting, random-matrix theory, inverse problems, early-stopping, training dynamics, Bolzmann Machine
|
We analyze the impact of limited data in the training of energy-based models, focusing on eigenmodes' dynamics, giving a theoretical perspective of early-stopping and data-correction protocols to improve the quality of the inferred model
| 13,303 |
2501.19158
|
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] | null |
511 |
Auto-reconfiguration for Latency Minimization in CPU-based DNN Serving
|
https://openreview.net/forum?id=Dg24PyeWsI
|
[
"Ankit Bhardwaj",
"Amar Phanishayee",
"Deepak Narayanan",
"Ryan Stutsman"
] |
Poster
|
In this paper, we investigate how to push the performance limits of serving Deep Neural Network (DNN) models on CPU-based servers. Specifically, we observe that while intra-operator parallelism across multiple threads is an effective way to reduce inference latency, it provides diminishing returns. Our primary insight is that instead of running a single instance of a model with all available threads on a server, running multiple instances each with smaller batch sizes and fewer threads for intra-op parallelism can provide lower inference latency. However, the right configuration is hard to determine manually since it is workload- (DNN model and batch size used by the serving system) and deployment-dependent (number of CPU cores on server). We present Packrat, a new serving system for online inference that given a model and batch size (𝐵) algorithmically picks the optimal number of instances (𝑖), the number of threads each should be allocated (𝑡), and the batch sizes each should operate on (𝑏) that minimizes latency. Packrat is built as an extension to TorchServe and supports online reconfigurations to avoid serving downtime. Averaged across a range of batch sizes, Packrat improves inference latency by 1.43× to 1.83× on a range of commonly used DNNs.
|
CPU, Inference
|
Our system Packrat improves CPU-based DNN inference for smaller models by automatically mapping batch-size and number of threads.
| 13,296 | null |
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] |
https://github.com/msr-fiddle/packrat
|
512 |
The Lock-in Hypothesis: Stagnation by Algorithm
|
https://openreview.net/forum?id=mE1M626qOo
|
[
"Tianyi Qiu",
"Zhonghao He",
"Tejasveer Chugh",
"Max Kleiman-Weiner"
] |
Poster
|
The training and deployment of large language models (LLMs) create a feedback loop with human users: models learn human beliefs from data, reinforce these beliefs with generated content, reabsorb the reinforced beliefs, and feed them back to users again and again. This dynamic resembles an echo chamber.
We hypothesize that this feedback loop entrenches the existing values and beliefs of users, leading to a loss of diversity in human ideas and potentially the *lock-in* of false beliefs.
We formalize this hypothesis and test it empirically with agent-based LLM simulations and real-world GPT usage data.
Analysis reveals sudden but sustained drops in diversity after the release of new GPT iterations, consistent with the hypothesized human-AI feedback loop.
*Website: https://thelockinhypothesis.com*
|
Value lock-in, Human-AI interaction, AI Alignment
|
Demonstrations of LLM-induced "lock-in" of user ideation, based on real-world data, simulations, and a formal model.
| 13,294 |
2506.06166
|
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] | null |
513 |
Wasserstein Flow Matching: Generative Modeling Over Families of Distributions
|
https://openreview.net/forum?id=MRmI68k3gd
|
[
"Doron Haviv",
"Aram-Alexandre Pooladian",
"Dana Pe'er",
"Brandon Amos"
] |
Poster
|
Generative modeling typically concerns transporting a single source distribution to a target distribution via simple probability flows. However, in fields like computer graphics and single-cell genomics, samples themselves can be viewed as distributions, where standard flow matching ignores their inherent geometry. We propose Wasserstein flow matching (WFM), which lifts flow matching onto families of distributions using the Wasserstein geometry. Notably, WFM is the first algorithm capable of generating distributions in high dimensions, whether represented analytically (as Gaussians) or empirically (as point-clouds). Our theoretical analysis establishes that Wasserstein geodesics constitute proper conditional flows over the space of distributions, making for a valid FM objective. Our algorithm leverages optimal transport theory and the attention mechanism, demonstrating versatility across computational regimes: exploiting closed-form optimal transport paths for Gaussian families, while using entropic estimates on point-clouds for general distributions. WFM successfully generates both 2D \& 3D shapes and high-dimensional cellular microenvironments from spatial transcriptomics data. Code is available at [WassersteinFlowMatching](https://github.com/WassersteinFlowMatching/WassersteinFlowMatching/).
|
Optimal Transport, Flow Matching, Generative Modeling, Single Cell
|
Flow Matching between distributions over distributions on the Wasserstein space for generation of Gaussians & general distributions.
| 13,287 |
2411.00698
|
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] | null |
514 |
Non-Asymptotic and Non-Lipschitzian Bounds on Optimal Values in Stochastic Optimization Under Heavy Tails
|
https://openreview.net/forum?id=XAFfOEFJG1
|
[
"Jindong Tong",
"Hongcheng Liu",
"Johannes O. Royset"
] |
Poster
|
This paper focuses on non-asymptotic confidence bounds (CB) for the optimal values of stochastic optimization (SO) problems. Existing approaches often rely on two conditions that may be restrictive: The need for a global Lipschitz constant and the assumption of light-tailed distributions. Beyond either of the conditions, it remains largely unknown whether computable CBs can be constructed. In view of this literature gap, we provide three key findings below: (i) Based on the conventional formulation of sample average approximation (SAA), we derive non-Lipschitzian CBs for convex SP problems under heavy tails. (ii) We explore diametrical risk minimization (DRM)---a recently introduced modification to SAA---and attain non-Lipschitzian CBs for nonconvex SP problems in light-tailed settings. (iii) We extend our analysis of DRM to handle heavy-tailed randomness by utilizing properties in formulations for training over-parameterized classification models.
|
Sample average approximation, diametrical risk minimization, confidence bound, heavy tails
|
This paper is focused on non-asymptotic and non-Lipschitzian confidence bounds for stochastic programming problems
| 13,286 | null |
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] | null |
515 |
Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods
|
https://openreview.net/forum?id=1xsW6tvMb3
|
[
"Yifan HAO",
"Xingyuan Pan",
"Hanning Zhang",
"Chenlu Ye",
"Rui Pan",
"Tong Zhang"
] |
Poster
|
Supervised fine-tuning (SFT) on domain-specific data is the dominant approach for adapting foundation models to specialized tasks. However, it has been observed that SFT models tend to forget knowledge acquired during pretraining. In vision models, ensembling a pretrained model with its fine-tuned counterpart has been shown to mitigate this issue. In this work, we demonstrate that the same holds for language models, and, more strikingly, we observe an overadaptation phenomenon: the ensemble model not only retains general knowledge from the foundation model but also outperforms the fine-tuned model even on the fine-tuning domain itself.
Despite the empirical success of ensembling, a theoretical understanding of its benefits remains underexplored. We develop a formal theoretical analysis of the overadaptation phenomenon.
Ensembling mitigates this by balancing two primary sources of error: bias, caused by insufficient fine-tuning, and variance, introduced by overfitting to fine-tuning data. While regularization techniques aim to address this trade-off, we show that ensembling provides a more effective solution. We analyze this phenomenon in over-parameterized linear settings and demonstrate that interpolating between pretrained and fine-tuned weights significantly improves performance. These findings offer theoretical justification for the observed advantages of model ensembling, supported by empirical experiments consistent with our analysis.
|
model ensemble, fine-tuning, multi-task
|
This paper mainly focuses on understanding the benefits of ensemble in supervised fine-tuning.
| 13,280 | null |
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] |
https://github.com/xypan0/LLMForgetting
|
516 |
TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
|
https://openreview.net/forum?id=0VvD1PmNzM
|
[
"Jingang QU",
"David Holzmüller",
"Gaël Varoquaux",
"Marine Le Morvan"
] |
Poster
|
The long-standing dominance of gradient-boosted decision trees on tabular data is currently challenged by tabular foundation models using In-Context Learning (ICL): setting the training data as context for the test data and predicting in a single forward pass without parameter updates. While TabPFNv2 foundation model excels on tables with up to 10K samples, its alternating column- and row-wise attentions make handling large training sets computationally prohibitive. So, can ICL be effectively scaled and deliver a benefit for larger tables? We introduce TabICL, a tabular foundation model for classification, pretrained on synthetic datasets with up to 60K samples and capable of handling 500K samples on affordable resources. This is enabled by a novel two-stage architecture: a column-then-row attention mechanism to build fixed-dimensional embeddings of rows, followed by a transformer for efficient ICL. Across 200 classification datasets from the TALENT benchmark, TabICL is on par with TabPFNv2 while being systematically faster (up to 10 times), and significantly outperforms all other approaches. On 53 datasets with over 10K samples, TabICL surpasses both TabPFNv2 and CatBoost, demonstrating the potential of ICL for large data. Pretraining code, inference code, and pre-trained models are available at https://github.com/soda-inria/tabicl.
|
Foundation Model, In-Context Learning, Tabular Data, Tabular Classification
| null | 13,277 |
2502.05564
|
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] |
https://github.com/soda-inria/tabicl
|
517 |
SCISSOR: Mitigating Semantic Bias through Cluster-Aware Siamese Networks for Robust Classification
|
https://openreview.net/forum?id=Rgd7poMTDp
|
[
"Shuo Yang",
"Bardh Prenkaj",
"Gjergji Kasneci"
] |
Poster
|
Shortcut learning undermines model generalization to out-of-distribution data. While the literature attributes shortcuts to biases in superficial features, we show that imbalances in the semantic distribution of sample embeddings induce spurious semantic correlations, compromising model robustness. To address this issue, we propose SCISSOR (Semantic Cluster Intervention for Suppressing ShORtcut), a Siamese network-based debiasing approach that remaps the semantic space by discouraging latent clusters exploited as shortcuts. Unlike prior data-debiasing approaches, SCISSOR eliminates the need for data augmentation and rewriting.
We evaluate SCISSOR on 6 models across 4 benchmarks: Chest-XRay and Not-MNIST in computer vision, and GYAFC and Yelp in NLP tasks. Compared to several baselines, SCISSOR reports +5.3 absolute points in F1 score on GYAFC, +7.3 on Yelp, +7.7 on Chest-XRay, and +1 on Not-MNIST. SCISSOR is also highly advantageous for lightweight models with ∼9.5% improvement on F1 for ViT on computer vision datasets and ∼11.9% for BERT on NLP. Our study redefines the landscape of model generalization by addressing overlooked semantic biases, establishing SCISSOR as a foundational framework for mitigating shortcut learning and fostering more robust, bias-resistant AI systems.
|
Semantic Bias, Shortcut Learning, Siamese Networks, Model Generalization, Debiasing
|
SCISSOR is a Siamese network-based debiasing method that mitigates semantic cluster imbalances, improving model robustness and generalization without data augmentation or rewriting.
| 13,272 |
2506.14587
|
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] |
https://github.com/shuoyangtum/scissor
|
518 |
Preference Controllable Reinforcement Learning with Advanced Multi-Objective Optimization
|
https://openreview.net/forum?id=49g4c8MWHy
|
[
"Yucheng Yang",
"Tianyi Zhou",
"Mykola Pechenizkiy",
"Meng Fang"
] |
Poster
|
Practical reinforcement learning (RL) usually requires agents to be optimized for multiple potentially conflicting criteria, e.g. speed vs. safety.
Although Multi-Objective RL (MORL) algorithms have been studied in previous works, their trained agents often cover limited Pareto optimal solutions and they lack precise controllability of the delicate trade-off among multiple objectives. Hence, the resulting agent is not versatile in aligning with customized requests from different users.
To bridge the gap, we develop the ``Preference controllable (PC) RL'' framework, which trains a preference-conditioned meta-policy that takes user preference as input controlling the generated trajectories within the preference region on the Pareto frontier. The PCRL framework is compatible with advanced Multi-Objective Optimization~(MOO) algorithms that are rarely seen in previous MORL approaches.
We also proposed a novel preference-regularized MOO algorithm specifically for PCRL. We provide a comprehensive theoretical analysis to justify its convergence and preference controllability.
We evaluate PCRL with different MOO algorithms against state-of-the-art MORL baselines in various challenging environments with up to six objectives. In these experiments, our proposed method exhibits significantly better controllability than existing approaches and can generate Pareto solutions with better diversity and utilities.
|
Multi-Objective Optimization, Multi-Objective Reinforcement Learning
|
Novel Multi-Objective Reinforcement Learning (MORL) method that discovers more Pareto optimal solutions than most previous MORL
| 13,270 | null |
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] | null |
519 |
Zero-Shot Generalization of GNNs over Distinct Attribute Domains
|
https://openreview.net/forum?id=R9iBZen9aY
|
[
"Yangyi Shen",
"Jincheng Zhou",
"Beatrice Bevilacqua",
"Joshua Robinson",
"Charilaos Kanatsoulis",
"Jure Leskovec",
"Bruno Ribeiro"
] |
Poster
|
Traditional Graph Neural Networks (GNNs) cannot generalize to new graphs with node attributes different from the training ones, making zero-shot generalization across different node attribute domains an open challenge in graph machine learning.
In this paper, we propose STAGE, which encodes *statistical dependencies* between attributes rather than individual attribute values, which may differ in test graphs. By assuming these dependencies remain invariant under changes in node attributes, STAGE achieves provable generalization guarantees for a family of domain shifts. Empirically, STAGE demonstrates strong zero-shot performance on medium-sized datasets: when trained on multiple graph datasets with different attribute spaces (varying in types and number) and evaluated on graphs with entirely new attributes, STAGE achieves a relative improvement in Hits@1 between 40% to 103% in link prediction and a 10% improvement in node classification compared to state-of-the-art baselines.
|
GNN, zero-shot, graph foundation models
|
We propose a novel method that can zero-shot generalize to test attributed graphs with different node features than those seen in train.
| 13,266 | null |
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] |
https://github.com/snap-stanford/stage-gnn
|
520 |
Synthetic Text Generation for Training Large Language Models via Gradient Matching
|
https://openreview.net/forum?id=mHySkOp46b
|
[
"Dang Nguyen",
"Zeman Li",
"Mohammadhossein Bateni",
"Vahab Mirrokni",
"Meisam Razaviyayn",
"Baharan Mirzasoleiman"
] |
Poster
|
Synthetic data has the potential to improve the performance, training efficiency, and privacy of real training examples. Nevertheless, existing approaches for synthetic text generation are mostly heuristics and cannot generate human-readable text without compromising the privacy of real data, or provide performance guarantees for training Large Language Models (LLMs). In this work, we propose the first theoretically rigorous approach for generating synthetic human-readable text that provides convergence, performance, and privacy guarantees for fine-tuning LLMs on a target task. To do so, we leverage Alternating Direction Method of Multipliers (ADMM) that iteratively optimizes the embeddings of synthetic examples to match the noisy gradient of the target training or validation data, and maps them to a sequence of text tokens with low perplexity. In doing so, the generated synthetic text guarantees convergence of the model to a close neighborhood of the solution obtained by fine-tuning on real data and preserves their privacy. Experiments on various classification tasks confirm the effectiveness of our proposed approach. Our code is available at [https://github.com/BigML-CS-UCLA/GRADMM](https://github.com/BigML-CS-UCLA/GRADMM).
|
Synthetic data, Large language models, Gradient matching, ADMM
|
We propose a new synthetic data generation approach with a theoretical guarantee using gradient matching.
| 13,263 |
2502.17607
|
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] |
https://github.com/BigML-CS-UCLA/GRADMM
|
521 |
Do Bayesian Neural Networks Actually Behave Like Bayesian Models?
|
https://openreview.net/forum?id=x5RQnF7Vw9
|
[
"Gábor Pituk",
"Vik Shirvaikar",
"Tom Rainforth"
] |
Poster
|
We empirically investigate how well popular approximate inference algorithms for Bayesian Neural Networks (BNNs) respect the theoretical properties of Bayesian belief updating. We find strong evidence on synthetic regression and real-world image classification tasks that common BNN algorithms such as variational inference, Laplace approximation, SWAG, and SGLD fail to update in a consistent manner, forget about old data under sequential updates, and violate the predictive coherence properties that would be expected of Bayesian methods. These observed behaviors imply that care should be taken when treating BNNs as true Bayesian models, particularly when using them beyond static prediction settings, such as for active, continual, or transfer learning.
|
Bayesian Neural Networks, BNNs, Bayesian Deep Learning
| null | 13,257 | null |
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] | null |
522 |
AUTOCIRCUIT-RL: Reinforcement Learning-Driven LLM for Automated Circuit Topology Generation
|
https://openreview.net/forum?id=NvYwrQbzOb
|
[
"Prashanth Vijayaraghavan",
"Luyao Shi",
"Ehsan Degan",
"Vandana Mukherjee",
"Xin Zhang"
] |
Poster
|
Analog circuit topology synthesis is integral to Electronic Design Automation (EDA), enabling the automated creation of circuit structures tailored to specific design requirements. However, the vast design search space and strict constraint adherence make efficient synthesis challenging. Leveraging the versatility of Large Language Models (LLMs), we propose AUTOCIRCUIT-RL, a novel reinforcement learning (RL)-based framework for automated analog circuit synthesis. The framework operates in two phases: instruction tuning, where an LLM learns to generate circuit topologies from structured prompts encoding design constraints, and RL refinement, which further improves the instruction-tuned model using reward models that evaluate validity, efficiency, and output voltage. The refined model is then used directly to generate topologies that satisfy the design constraints. Empirical results show that AUTOCIRCUIT-RL generates ~12% more valid circuits and improves efficiency by ~14% compared to the best baselines, while reducing duplicate generation rates by ~38%. It achieves over 60% success in synthesizing valid circuits with limited training data, demonstrating strong generalization. These findings highlight the framework's effectiveness in scaling to complex circuits while maintaining efficiency and constraint adherence, marking a significant advancement in AI-driven circuit design.
|
Analog circuit, circuit topology, reinforcement learning, instruction tuning, RL refinement
|
We introduce AUTOCIRCUIT-RL, an AI system that uses reinforcement learning and large language models to automatically generate efficient and valid analog circuit designs from constraints.
| 13,250 | null |
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] | null |
523 |
What If We Recaption Billions of Web Images with LLaMA-3?
|
https://openreview.net/forum?id=Hntp7s2YfF
|
[
"Xianhang Li",
"Haoqin Tu",
"Mude Hui",
"Zeyu Wang",
"Bingchen Zhao",
"Junfei Xiao",
"Sucheng Ren",
"Jieru Mei",
"Qing Liu",
"Huangjie Zheng",
"Yuyin Zhou",
"Cihang Xie"
] |
Poster
|
Web-crawled image-text pairs are inherently noisy. Prior studies demonstrate that semantically aligning and enriching textual descriptions of these pairs can significantly enhance model training across various vision-language tasks, particularly text-to-image generation. However, large-scale investigations in this area remain predominantly closed-source. Our paper aims to bridge this community effort, leveraging the powerful and $\textit{open-sourced}$ LLaMA-3, a GPT-4 level LLM. Our recaptioning pipeline is simple: first, we fine-tune a LLaMA-3-8B powered LLaVA-1.5 and then employ it to recaption ~1.3 billion images from the DataComp-1B dataset. Our empirical results confirm that this enhanced dataset, Recap-DataComp-1B, offers substantial benefits in training advanced vision-language models. For discriminative models like CLIP, we observe an average of 3.1% enhanced zero-shot performance cross four cross-modal retrieval tasks using a mixed set of the original and our captions. For generative models like text-to-image Diffusion Transformers, the generated images exhibit a significant improvement in alignment with users' text instructions, especially in following complex queries. Our project page is https://www.haqtu.me/Recap-Datacomp-1B/.
|
image-text datasets; synthetic captions
|
We propose Recap-DataComp-1B, the first publicly available image-text dataset with LLaMA-3–generated synthetic captions scaled to the billion level.
| 13,249 | null |
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] |
https://github.com/UCSC-VLAA/Recap-DataComp-1B
|
524 |
A Bregman Proximal Viewpoint on Neural Operators
|
https://openreview.net/forum?id=lzzPAQ1TxA
|
[
"Abdel-Rahim Mezidi",
"Jordan Patracone",
"Saverio Salzo",
"Amaury Habrard",
"Massimiliano Pontil",
"Rémi Emonet",
"Marc Sebban"
] |
Poster
|
We present several advances on neural operators by viewing the action of operator layers as the minimizers of Bregman regularized optimization problems over Banach function spaces. The proposed framework allows interpreting the activation operators as Bregman proximity operators from dual to primal space. This novel viewpoint is general enough to recover classical neural operators as well as a new variant, coined Bregman neural operators, which includes the inverse activation operator and features the same expressivity of standard neural operators. Numerical experiments support the added benefits of the Bregman variant of Fourier neural operators for training deeper and more accurate models.
|
neural operators, proximal optimization, bregman divergence, fourier neural operator
| null | 13,247 | null |
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] |
https://github.com/armezidi/bregmano
|
525 |
On the Robustness of Reward Models for Language Model Alignment
|
https://openreview.net/forum?id=Tf4lRAOGkj
|
[
"Jiwoo Hong",
"Noah Lee",
"Eunki Kim",
"Guijin Son",
"Woojin Chung",
"Aman Gupta",
"Shao Tang",
"James Thorne"
] |
Poster
|
The Bradley-Terry (BT) model is widely practiced in reward modeling for reinforcement learning with human feedback (RLHF). Despite its effectiveness, reward models (RMs) trained with BT model loss as one-way classifiers are prone to over-optimization, losing generalizability to unseen inputs. In this paper, we study the cause of over-optimization and its downstream effects on the RLHF procedure, highlighting the importance of robustness in RMs. First, we show that the excessive dispersion of hidden state norms is the main source of over-optimization. Correspondingly, we propose batch-wise sum-to-zero regularization (BSR) that enforces reward sum for each batch to be zero-centered, constraining the rewards with abnormally large magnitudes. We assess the impact of BSR in improving robustness in RMs through four scenarios of over-optimization, where BSR consistently manifests better robustness on unseen inputs. Then, we compare the plain BT model and BSR on RLHF training and empirically show that robust RMs better align the policy to the gold preference model. Finally, we apply BSR to high-quality data and models, which surpasses state-of-the-art RMs in the 8B scale by adding more than 5\% in complex preference prediction tasks. By conducting RLOO training with 8B RMs, AlpacaEval 2.0, with reducing generation length by 40\% while adding a 7\% increase in win rate, further highlights that robustness in RMs induces robustness in RLHF training.
|
RLHF, Reward modeling, Over-optimization
|
We analyze the Bradley-Terry model as a source of reward model over-optimization and propose batch-wise sum-to-zero (BatchSum) regularization,
| 13,241 |
2505.07271
|
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] |
https://github.com/LinkedIn-XFACT/RM-Robustness
|
526 |
From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications
|
https://openreview.net/forum?id=fsf7LhbYdf
|
[
"AJAY KUMAR JAISWAL",
"Yifan Wang",
"Lu Yin",
"Shiwei Liu",
"Runjin Chen",
"Jiawei Zhao",
"Ananth Grama",
"Yuandong Tian",
"Zhangyang Wang"
] |
Poster
|
Large Language Models (LLMs) matrices can often be expressed in low-rank format with potential to relax memory and compute resource requirements. Unlike previous works which pivot around developing novel matrix decomposition algorithms, in this work we focus to study the emerging non-uniform low-rank properties across weight matrices in LLMs through the lens of stabilizing gradient subspace. \textit{Firstly,} we provide a theoretical framework to understand the stabilization of gradient subspaces through Hessian analysis. \textit{Secondly,} we empirically establish a consequential relationship between the gradient dynamics and low-rank expressiveness of weight matrices. Our findings reveal that different LLM components exhibit varying levels of converged low-rank structure, necessitating a non-uniform rank reduction across them to minimize performance drop due to compression. In view of that, we present \textit{Weight Low-Rank Projection} \textbf{(WeLore)} that unifies weight compression and memory-efficient fine-tuning as ONE, in a data-agnostic and one-shot way. Going beyond only as a compression technique, WeLore categorizes weight matrices into Low-rank Components (LRCs) and Non-Low-rank Components (N-LRCs) based on their ability to express themselves as low-rank. Our gradient dynamics perspective illustrate that \textit{LRCs tend to have better finetuning capabilities} and their standalone finetuning can closely mimic (sometimes outperform) the training loss trajectory and performance of full-finetuning with notable memory and compute footprint reduction. All codes and checkpoints will be released.
|
Large language models, Gradient subspace, Memory-efficient training, Optimization, Low Rank Compression
|
Our work provides a theoretical perspective of gradient subspace stabilization and present WeLore as a novel framework for LLM compression and memory-efficient finetuning.
| 13,235 |
2407.11239
|
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] |
https://github.com/VITA-Group/WeLore
|
527 |
QUTE: Quantifying Uncertainty in TinyML models with Early-exit-assisted ensembles for model-monitoring
|
https://openreview.net/forum?id=EIEpGfixF6
|
[
"Nikhil Pratap Ghanathe",
"Steven J E Wilton"
] |
Poster
|
Uncertainty quantification (UQ) provides a resource-efficient solution for on-device monitoring of tinyML models deployed remotely without access to true labels. However, existing UQ methods impose significant memory and compute demands, making them impractical for ultra-low-power, KB-sized tinyML devices. Prior work has attempted to reduce overhead by using early-exit ensembles to quantify uncertainty in a single forward pass, but these approaches still carry prohibitive costs. To address this, we propose QUTE, a novel resource-efficient early-exit-assisted ensemble architecture optimized for tinyML models. QUTE introduces additional output blocks at the final exit of the base network, distilling early-exit knowledge into these blocks to form a diverse yet lightweight ensemble. We show that QUTE delivers superior uncertainty quality on tiny models, achieving comparable performance on larger models with 59% smaller model sizes than the closest prior work. When deployed on a microcontroller, QUTE demonstrates a 31% reduction in latency on average. In addition, we show that QUTE excels at detecting accuracy-drop events, outperforming all prior works.
|
ML Model monitoring, TinyML, Resource-efficient learning, Uncertainty quantification, Early-exit networks, Trust in ML, Lightweight neural networks, Ensemble learning, Failure detection
|
QUTE is a resource-efficient early-exit ensemble method for on-device uncertainty quantification (UQ) and monitoring in tinyML, reducing model size by 59% and latency by 31%, while detecting accuracy drops. It fits on a 256KB microcontroller.
| 13,233 |
2404.12599
|
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] | null |
528 |
Diverging Preferences: When do Annotators Disagree and do Models Know?
|
https://openreview.net/forum?id=qWgAAVhoXb
|
[
"Michael JQ Zhang",
"Zhilin Wang",
"Jena D. Hwang",
"Yi Dong",
"Olivier Delalleau",
"Yejin Choi",
"Eunsol Choi",
"Xiang Ren",
"Valentina Pyatkin"
] |
Poster
|
We examine diverging preferences in human-labeled preference datasets. We develop a taxonomy of disagreement sources spanning ten categories across four high-level classes and find that the majority of disagreements are due to factors such as task underspecification or response style. Our findings challenge a standard assumption in reward modeling methods that annotator disagreements can be attributed to simple noise. We then explore how these findings impact two areas of LLM development: reward modeling training and evaluation. In our experiments, we demonstrate how standard reward modeling (e.g., Bradley-Terry) and LLM-as-Judge evaluation methods fail to account for divergence between annotators. These findings highlight challenges in LLM evaluations, which are greatly influenced by divisive features like response style, and in developing pluralistically aligned LLMs. To address these issues, we develop methods for identifying diverging preferences to mitigate their influence in evaluations and during LLM training.
|
RLHF, Pluralistic Alignment
|
We examine diverging preferences in human-labeled preference datasets and their influences in reward modeling and LLM evaluations.
| 13,232 |
2410.14632
|
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] | null |
529 |
FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for Enabling Fair LLM-Based Recommender Systems
|
https://openreview.net/forum?id=edN2rEemj6
|
[
"Arya Fayyazi",
"Mehdi Kamal",
"Massoud Pedram"
] |
Poster
|
We propose FACTER, a fairness-aware framework for LLM-based recommendation systems that integrates conformal prediction with dynamic prompt engineering. By introducing an adaptive semantic variance threshold and a violation-triggered mechanism, FACTER automatically tightens fairness constraints whenever biased patterns emerge. We further develop an adversarial prompt generator that leverages historical violations to reduce repeated demographic biases without retraining the LLM. Empirical results on MovieLens and Amazon show that FACTER substantially reduces fairness violations (up to 95.5%) while maintaining strong recommendation accuracy, revealing semantic variance as a potent proxy of bias.
|
Large Language Models, Conformal Prediction, Fairness, Repair, Embedding, Bias, Prompt Engineering, Adversarial
| null | 13,223 |
2502.02966
|
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] |
https://github.com/AryaFayyazi/FACTER
|
530 |
Adaptive Partitioning Schemes for Optimistic Optimization
|
https://openreview.net/forum?id=rA2P4H7Dep
|
[
"Raja Sunkara",
"Ardhendu Tripathy"
] |
Poster
|
Applications such as engineering design often require us to optimize a black-box function, i.e., a system whose inner processing is not analytically known and whose gradients are not available. Practitioners often have a fixed budget for the number of function evaluations and the performance of an optimization algorithm is measured by its simple regret. In this paper, we study the class of ``Optimistic Optimization'' algorithms for black-box optimization that use a partitioning scheme for the domain. We develop algorithms that learn a good partitioning scheme and use flexible surrogate models such as neural networks in the optimization procedure. For multi-index functions on an $m$-dimensional subspace within $d$ dimensions, our algorithm attains $\tilde{O}(n^{-\beta / d})$ regret, where $\beta = 1 + \frac{d-m}{2m-1}$, as opposed to $\tilde{O}(n^{-1/d})$ for SequOOL, a state-of-the-art optimistic optimization algorithm. Our approach is competitive across a wide range of numerical benchmarks. Additionally, we introduce weight quantization in a large language model as a novel task for black-box optimization. Our approach improves the quality of Activation-aware Weight Quantization (AWQ) of the OPT-1.3B model, achieving an approximate 10\% improvement in performance relative to the best possible unquantized model.
|
discrete optimization, derivative-free optimization, black-box function, regret bounds, subspace learning, neural networks, surrogate modeling
| null | 13,222 | null |
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] |
https://github.com/raja-sunkara/Learned-Partitions-SequOOL
|
531 |
Zero-Shot Adaptation of Parameter-Efficient Fine-Tuning in Diffusion Models
|
https://openreview.net/forum?id=osNUbJ4vlX
|
[
"Farzad Farhadzadeh",
"Debasmit Das",
"Shubhankar Borse",
"Fatih Porikli"
] |
Poster
|
We introduce ProLoRA, enabling zero-shot adaptation of parameter-efficient fine-tuning in text-to-image diffusion models. ProLoRA transfers pre-trained low-rank adjustments (e.g., LoRA) from a source to a target model without additional training data. This overcomes the limitations of traditional methods that require retraining when switching base models, often challenging due to data constraints. ProLoRA achieves this via projection of source adjustments into the target model's weight space, leveraging subspace and null space similarities and selectively targeting aligned layers. Evaluations on established text-to-image models demonstrate successful knowledge transfer and comparable performance without retraining.
|
Diffusion Model, parameter efficient fine tuning, Low rank adaptation, transfer learning
|
Transfer pre-trained low-rank adjustments (like LoRA) between diffusion models without retraining by projecting them into the new model's weight space. No need for original training data!
| 13,217 |
2506.04244
|
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] | null |
532 |
Self-Supervised Transformers as Iterative Solution Improvers for Constraint Satisfaction
|
https://openreview.net/forum?id=IQN6ID0snT
|
[
"Yudong Xu",
"Wenhao Li",
"Scott Sanner",
"Elias Boutros Khalil"
] |
Poster
|
We present a Transformer-based framework for Constraint Satisfaction Problems (CSPs).
CSPs find use in many applications and thus accelerating their solution with machine learning is of wide interest. Most existing approaches rely on supervised learning from feasible solutions or reinforcement learning, paradigms that require either feasible solutions to these NP-Complete CSPs or large training budgets and a complex expert-designed reward signal. To address these challenges, we propose ConsFormer, a self-supervised framework that leverages a Transformer as a solution refiner. ConsFormer constructs a solution to a CSP iteratively in a process that mimics local search. Instead of using feasible solutions as labeled data, we devise differentiable approximations to the discrete constraints of a CSP to guide model training. Our model is trained to improve random assignments for a single step but is deployed iteratively at test time, circumventing the bottlenecks of supervised and reinforcement learning. Experiments on Sudoku, Graph Coloring, Nurse Rostering, and MAXCUT demonstrate that our method can tackle out-of-distribution CSPs simply through additional iterations.
|
Constraint Satisfaction Problem, Transformer, Self-supervised Learning, Local Search
|
A self-supervised Transformer-based framework for iteratively solving Constraint Satisfaction Problems.
| 13,215 |
2502.15794
|
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] |
https://github.com/khalil-research/ConsFormer
|
533 |
MIB: A Mechanistic Interpretability Benchmark
|
https://openreview.net/forum?id=sSrOwve6vb
|
[
"Aaron Mueller",
"Atticus Geiger",
"Sarah Wiegreffe",
"Dana Arad",
"Iván Arcuschin",
"Adam Belfki",
"Yik Siu Chan",
"Jaden Fried Fiotto-Kaufman",
"Tal Haklay",
"Michael Hanna",
"Jing Huang",
"Rohan Gupta",
"Yaniv Nikankin",
"Hadas Orgad",
"Nikhil Prakash",
"Anja Reusch",
"Aruna Sankaranarayanan",
"Shun Shao",
"Alessandro Stolfo",
"Martin Tutek",
"et al. (3 additional authors not shown)"
] |
Poster
|
How can we know whether new mechanistic interpretability methods achieve real improvements?
In pursuit of lasting evaluation standards, we propose MIB, a Mechanistic Interpretability Benchmark, with two tracks spanning four tasks and five models. MIB favors methods that precisely and concisely recover relevant causal pathways or causal variables in neural language models. The circuit localization track compares methods that locate the model components---and connections between them---most important for performing a task (e.g., attribution patching or information flow routes). The causal variable track compares methods that featurize a hidden vector, e.g., sparse autoencoders (SAE) or distributed alignment search (DAS), and align those features to a task-relevant causal variable. Using MIB, we find that attribution and mask optimization methods perform best on circuit localization. For causal variable localization, we find that the supervised DAS method performs best, while SAEs features are not better than neurons, i.e., non-featurized hidden vectors. These findings illustrate that MIB enables meaningful comparisons, and increases our confidence that there has been real progress in the field.
|
interpretability, causality, localization, benchmarking, circuits
|
We propose a benchmark to establish lasting standards for comparing causal localization methods.
| 13,210 |
2504.13151
|
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0.007016246672719717,
-0.024894623085856438,
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-0.06985295563936234,
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] |
https://github.com/aaronmueller/MIB
|
534 |
Convergence Analysis of Policy Gradient Methods with Dynamic Stochasticity
|
https://openreview.net/forum?id=XEYoTQv00G
|
[
"Alessandro Montenegro",
"Marco Mussi",
"Matteo Papini",
"Alberto Maria Metelli"
] |
Poster
|
*Policy gradient* (PG) methods are effective *reinforcement learning* (RL) approaches, particularly for continuous problems. While they optimize stochastic (hyper)policies via action- or parameter-space exploration, real-world applications often require deterministic policies. Existing PG convergence guarantees to deterministic policies assume a fixed stochasticity in the (hyper)policy, tuned according to the desired final suboptimality, whereas practitioners commonly use a dynamic stochasticity level.
This work provides the theoretical foundations for this practice. We introduce PES, a phase-based method that reduces stochasticity via a deterministic schedule while running PG subroutines with fixed stochasticity in each phase. Under gradient domination assumptions, PES achieves last-iterate convergence to the optimal deterministic policy with a sample complexity of order $\widetilde{\mathcal{O}}(\epsilon^{-5})$.
Additionally, we analyze the common practice, termed SL-PG, of jointly learning stochasticity (via an appropriate parameterization) and (hyper)policy parameters. We show that SL-PG also ensures last-iterate convergence with a rate $\widetilde{\mathcal{O}}(\epsilon^{-3})$, but to the optimal stochastic (hyper)policy only, requiring stronger assumptions compared to PES.
|
reinforcement learning, policy gradients, convergence, deterministic policies
|
Study of last-iterate convergence guarantees to the optimal deterministic policy of policy gradients dynamically adjusting the stochasticity of the (hyper)policy.
| 13,207 | null |
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] |
https://github.com/MontenegroAlessandro/MagicRL
|
535 |
Enforcing Idempotency in Neural Networks
|
https://openreview.net/forum?id=jk8vYZLty7
|
[
"Nikolaj Banke Jensen",
"Jamie Vicary"
] |
Poster
|
In this work, we propose a new architecture-agnostic method for training idempotent neural networks. An idempotent operator satisfies $f(x) = f(f(x))$, meaning it can be applied iteratively with no effect beyond the first application. Some neural networks used in data transformation tasks, such as image generation and augmentation, can represent non-linear idempotent projections. Using methods from perturbation theory we derive the recurrence relation ${\mathbf{K}' \leftarrow 3\mathbf{K}^2 - 2\mathbf{K}^3}$ for iteratively projecting a real-valued matrix $\mathbf{K}$ onto the manifold of idempotent matrices. Our analysis shows that for linear, single-layer MLP networks this projection 1) has idempotent fixed points, and 2) is attracting only around idempotent points. We give an extension to non-linear networks by considering our approach as a substitution of the gradient for the canonical loss function, achieving an architecture-agnostic training scheme. We provide experimental results for MLP- and CNN-based architectures with significant improvement in idempotent error over the canonical gradient-based approach. Finally, we demonstrate practical applications of the method as we train a generative network successfully using only a simple reconstruction loss paired with our method.
|
idempotent neural networks, non-convex optimisation, gradient-free optimisation
|
An alternative optimisation method to gradient-based approaches for finding idempotent neural networks is proposed and evaluated.
| 13,202 | null |
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] | null |
536 |
Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation
|
https://openreview.net/forum?id=z63Ot0pcM3
|
[
"Mingyu Kang",
"Yong Suk Choi"
] |
Poster
|
Text-to-image diffusion models have achieved remarkable success in generating high-quality and diverse images. Building on these advancements, diffusion models have also demonstrated exceptional performance in text-guided image editing. A key strategy for effective image editing involves inverting the source image into editable noise maps associated with the target image. However, previous inversion methods face challenges in adhering closely to the target text prompt. The limitation arises because inverted noise maps, while enabling faithful reconstruction of the source image, restrict the flexibility needed for desired edits. To overcome this issue, we propose Editable Noise Map Inversion (ENM Inversion), a novel inversion technique that searches for optimal noise maps to ensure both content preservation and editability. We analyze the properties of noise maps for enhanced editability. Based on this analysis, our method introduces an editable noise refinement that aligns with the desired edits by minimizing the difference between the reconstructed and edited noise maps. Extensive experiments demonstrate that ENM Inversion outperforms existing approaches across a wide range of image editing tasks in both preservation and edit fidelity with target prompts. Our approach can also be easily applied to video editing, enabling temporal consistency and content manipulation across frames.
|
diffusion models, image editing, diffusion inversion
|
We introduce ENM Inversion, a technique that improves text-guided image editing by enhancing noise map editability.
| 13,199 | null |
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] | null |
537 |
The Diffusion Duality
|
https://openreview.net/forum?id=9P9Y8FOSOk
|
[
"Subham Sekhar Sahoo",
"Justin Deschenaux",
"Aaron Gokaslan",
"Guanghan Wang",
"Justin T Chiu",
"Volodymyr Kuleshov"
] |
Poster
|
Uniform-state discrete diffusion models hold the promise of fast text generation due to their inherent ability to self-correct. However, they are typically outperformed by autoregressive models and masked diffusion models. In this work, we narrow this performance gap by leveraging a key insight: Uniform-state diffusion processes naturally emerge from an underlying Gaussian diffusion. Our method, Duo, transfers powerful techniques from Gaussian diffusion to improve both training and sampling. First, we introduce a curriculum learning strategy guided by the Gaussian process, **doubling training speed** by reducing variance. Models trained with curriculum learning surpass autoregressive models in zero-shot perplexity on 3 of 7 benchmarks.
Second, we present Discrete Consistency Distillation, which adapts consistency distillation from the continuous to the discrete setting. This algorithm **unlocks few-step generation in diffusion language models** by accelerating sampling by two orders of magnitude. We provide the code and model checkpoints on the project page: http://s-sahoo.github.io/duo
|
diffusion language models, diffusion models, large language models, distillation
|
We prove that discrete diffusion processes admit an underlying Gaussian diffusion formulation which enables the design of faster training and sampling algorithms.
| 13,196 |
2506.10892
|
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] |
https://github.com/s-sahoo/duo
|
538 |
Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes
|
https://openreview.net/forum?id=vZhdXRnfPU
|
[
"Erica Zhang",
"Fangzhao Zhang",
"Mert Pilanci"
] |
Poster
|
Active learning methods aim to improve sample complexity in machine learning. In this work, we investigate an active learning scheme via a novel gradient-free cutting-plane training method for ReLU networks of arbitrary depth and develop a convergence theory.
We demonstrate, for the first time, that cutting-plane algorithms, traditionally used in linear models, can be extended to deep neural networks despite their nonconvexity and nonlinear decision boundaries. Moreover, this training method induces the first deep active learning scheme known to achieve convergence guarantees, revealing a geometric contraction rate of the feasible set. We exemplify the effectiveness of our proposed active learning method against popular deep active learning baselines via both synthetic data experiments and sentimental classification task on real datasets.
|
convex optimization, cutting plane method, active learning
| null | 13,191 |
2410.02145
|
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] |
https://github.com/pilancilab/cpal
|
539 |
Eliciting Language Model Behaviors with Investigator Agents
|
https://openreview.net/forum?id=AulTigiaMv
|
[
"Xiang Lisa Li",
"Neil Chowdhury",
"Daniel D. Johnson",
"Tatsunori Hashimoto",
"Percy Liang",
"Sarah Schwettmann",
"Jacob Steinhardt"
] |
Poster
|
Language models exhibit complex, diverse behaviors when prompted with free-form text, making it hard to characterize the space of possible outputs. We study the problem of behavioral elicitation, where the goal is to search for prompts that induce specific target behaviors (e.g., hallucinations, harmful responses) from a target language model. To navigate the exponentially large space of possible prompts, we train amortized investigator models to emulate the posterior distribution over the prompts, conditioned on the target behavior. Specifically, we first fit a reverse model and then use reinforcement learning to optimize likelihood of generating the target behavior. To improve the diversity of the prompt distribution, we further propose a novel iterative training objective based on the Frank-Wolfe algorithm that encourages each iteration to discover different sets of prompts not captured by previous iterations. Our investigator models produce prompts that exhibit a variety of effective and human-interpretable strategies for behavior elicitation, obtaining a 100% attack success rate on AdvBench (Harmful Behaviors) and an 85% hallucination rate.
|
red teaming; posterior inference
| null | 13,189 |
2502.01236
|
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] | null |
540 |
Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias
|
https://openreview.net/forum?id=7ywj1B3DuO
|
[
"Yuanzhe Hu",
"Kinshuk Goel",
"Vlad Killiakov",
"Yaoqing Yang"
] |
Poster
|
Diagnosing deep neural networks (DNNs) through the eigenspectrum of weight matrices has been an active area of research in recent years. At a high level, eigenspectrum analysis of DNNs involves measuring the heavytailness of the empirical spectral densities (ESD) of weight matrices. It provides insight into how well a model is trained and can guide decisions on assigning better layer-wise training hyperparameters. In this paper, we address a challenge associated with such eigenspectrum methods: the impact of the aspect ratio of weight matrices on estimated heavytailness metrics. We demonstrate that matrices of varying sizes (and aspect ratios) introduce a non-negligible bias in estimating heavytailness metrics, leading to inaccurate model diagnosis and layer-wise hyperparameter assignment. To overcome this challenge, we propose FARMS (Fixed-Aspect-Ratio Matrix Subsampling), a method that normalizes the weight matrices by subsampling submatrices with a fixed aspect ratio. Instead of measuring the heavytailness of the original ESD, we measure the average ESD of these subsampled submatrices. We show that measuring the heavytailness of these submatrices with the fixed aspect ratio can effectively mitigate the aspect ratio bias. We validate our approach across various optimization techniques and application domains that involve eigenspectrum analysis of weights, including image classification in computer vision (CV) models, scientific machine learning (SciML) model training, and large language model (LLM) pruning. Our results show that despite its simplicity, FARMS uniformly improves the accuracy of eigenspectrum analysis while enabling more effective layer-wise hyperparameter assignment in these application domains. In one of the LLM pruning experiments, FARMS reduces the perplexity of the LLaMA-7B model by 17.3\% when compared with the state-of-the-art method.
|
LLM Pruning, Weight Matrix Analysis, Spectral Analysis, Marchenko-Pastur Law, Hyperparameter Tuning
|
We reduce the bias of eigenspectrum diagnosis using a method based on matrix subsampling
| 13,188 |
2506.06280
|
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] |
https://github.com/HUST-AI-HYZ/FARMS
|
541 |
A Machine Learning Approach to Duality in Statistical Physics
|
https://openreview.net/forum?id=bWeLpOgqGp
|
[
"Prateek Gupta",
"Andrea E. V. Ferrari",
"Nabil Iqbal"
] |
Poster
|
The notion of duality -- that a given physical system can have two different mathematical descriptions -- is a key idea in modern theoretical physics. Establishing a duality in lattice statistical mechanics models requires the construction of a dual Hamiltonian and a map from the original to the dual observables. By using neural networks to parameterize these maps and introducing a loss function that penalises the difference between correlation functions in original and dual models, we formulate the process of duality discovery as an optimization problem. We numerically solve this problem and show that our framework can rediscover the celebrated Kramers-Wannier duality for the 2d Ising model, numerically reconstructing the known mapping of temperatures. We further investigate the 2d Ising model deformed by a plaquette coupling and find families of ``approximate duals''. We discuss future directions and prospects for discovering new dualities within this framework.
|
Duality, Statistical Mechanics
|
We formulate the discovery of dualities in lattice statistical mechanics as an optimization problem
| 13,182 | null |
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] |
https://github.com/pg2455/physics_duality
|
542 |
Resolving Lexical Bias in Model Editing
|
https://openreview.net/forum?id=aPm6SfcMWQ
|
[
"Hammad Rizwan",
"Domenic Rosati",
"Ga Wu",
"Hassan Sajjad"
] |
Poster
|
Model editing aims to modify the outputs of large language models after they are trained. Previous approaches have often involved direct alterations to model weights, which can result in model degradation. Recent techniques avoid making modifications to the model's weights by using an adapter that applies edits to the model when triggered by semantic similarity in the representation space. We demonstrate that current adapter methods are *critically vulnerable* to strong lexical biases, leading to issues such as applying edits to irrelevant prompts with overlapping words. This paper presents a principled approach to learning a disentangled representation space that facilitates precise localization of edits by maintaining distance between irrelevant prompts while preserving proximity among paraphrases. In our empirical study, we show that our method (Projector Editor Networks for Model Editing - PENME) achieves state-of-the-art model editing results while being more computationally efficient during inference than previous methods and adaptable across different architectures.
|
Model Editing, Contrastive Learning, Representation Learning
|
PENME tackles the critical vulnerability of model editing techniques by learning a representation space that prevents erroneous edit retrival based on representation similarities.
| 13,175 |
2408.10411
|
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] |
https://github.com/hammadrizwan/PENME
|
543 |
Adaptive Elicitation of Latent Information Using Natural Language
|
https://openreview.net/forum?id=I7N6vtUChM
|
[
"Jimmy Wang",
"Thomas P Zollo",
"Richard Zemel",
"Hongseok Namkoong"
] |
Poster
|
Eliciting information to reduce uncertainty about a latent entity is a critical task in many application domains, e.g., assessing individual student learning outcomes, diagnosing underlying diseases, or learning user preferences. Though natural language is a powerful medium for this purpose, large language models (LLMs) and existing fine-tuning algorithms lack mechanisms for strategically gathering information to refine their own understanding of the latent entity. To harness the generalization power and world knowledge of LLMs in developing effective information-gathering strategies, we propose an adaptive elicitation framework that actively reduces uncertainty on the latent entity. Since probabilistic modeling of an abstract latent entity is difficult, our framework adopts a predictive view of uncertainty, using a meta-learned language model to simulate future observations and enable scalable uncertainty quantification over complex natural language. Through autoregressive forward simulation, our model quantifies how new questions reduce epistemic uncertainty, enabling the development of sophisticated information-gathering strategies to choose the most informative next queries. In experiments on the 20 questions game, dynamic opinion polling, and adaptive student assessment, our method consistently outperforms baselines in identifying critical unknowns and improving downstream predictions, illustrating the promise of strategic information gathering in natural language settings.
|
Uncertainty Quantification, Natural Language, Adaptive Survey Design
|
We introduce a novel meta-learning and planning algorithm that can use LLMs to quantify uncertainty over natural language inputs, and ask questions to elicit information that reduces this uncertainty.
| 13,154 |
2504.04204
|
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] |
https://github.com/namkoong-lab/adaptive-elicitation
|
544 |
Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures
|
https://openreview.net/forum?id=Rcivp36KzO
|
[
"Jie Gao",
"Rajesh Jayaram",
"Benedikt Kolbe",
"Shay Sapir",
"Chris Schwiegelshohn",
"Sandeep Silwal",
"Erik Waingarten"
] |
Poster
|
Randomized dimensionality reduction is a widely-used algorithmic technique for speeding up large-scale Euclidean optimization problems. In this paper, we study dimension reduction for a variety of maximization problems, including max-matching, max-spanning tree, as well as various measures for dataset diversity. For these problems, we show that the effect of dimension reduction is intimately tied to the *doubling dimension* $\lambda_X$ of the underlying dataset $X$---a quantity measuring intrinsic dimensionality of point sets. Specifically, the dimension required is $O(\lambda_X)$, which we also show is necessary for some of these problems. This is in contrast to classical dimension reduction results, whose dependence grow with the dataset size $|X|$. We also provide empirical results validating the quality of solutions found in the projected space, as well as speedups due to dimensionality reduction.
|
Dimensionality Reduction, Dimension Reduction, Geometric Optimization, Johnson Lindenstrauss, Diversity Maximization, Diversity
|
Novel dimensionality reduction results for geometric maximization problems
| 13,149 |
2506.00165
|
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] | null |
545 |
DIS-CO: Discovering Copyrighted Content in VLMs Training Data
|
https://openreview.net/forum?id=q0P4rrDImq
|
[
"André V. Duarte",
"Xuandong Zhao",
"Arlindo L. Oliveira",
"Lei Li"
] |
Poster
|
*How can we verify whether copyrighted content was used to train a large vision-language model (VLM) without direct access to its training data?* Motivated by the hypothesis that a VLM is able to recognize images from its training corpus, we propose DIS-CO, a novel approach to infer the inclusion of copyrighted content during the model's development. By repeatedly querying a VLM with specific frames from targeted copyrighted material, DIS-CO extracts the content's identity through free-form text completions. To assess its effectiveness, we introduce MovieTection, a benchmark comprising 14,000 frames paired with detailed captions, drawn from films released both before and after a model’s training cutoff. Our results show that DIS-CO significantly improves detection performance, nearly doubling the average AUC of the best prior method on models with logits available. Our findings also highlight a broader concern: all tested models appear to have been exposed to some extent to copyrighted content. We provide the code in the supplementary materials.
|
Copyrighted Content Detection, Membership Inference Attacks, Large Vision Language Models, Natural Language Processing
|
DIS-CO identifies copyrighted content in VLMs training data by showing that models can link movie frames to their titles in a free-form text generation setting, even when the frames are highly challenging, suggesting prior exposure during training.
| 13,148 | null |
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] |
https://github.com/avduarte333/DIS-CO
|
546 |
MetaAgent: Automatically Constructing Multi-Agent Systems Based on Finite State Machines
|
https://openreview.net/forum?id=vOxaD3hhPt
|
[
"Yaolun Zhang",
"Xiaogeng Liu",
"Chaowei Xiao"
] |
Poster
|
Large Language Models (LLMs) have demonstrated the ability to solve a wide range of practical tasks within multi-agent systems. However, existing human-designed multi-agent frameworks are typically limited to a small set of pre-defined scenarios, while current automated design methods suffer from several limitations, such as the lack of tool integration, dependence on external training data, and rigid communication structures. In this paper, we propose \textbf{MetaAgent}, a \textbf{finite state machine} based framework that can automatically generate a multi-agent system. Given a task description, MetaAgent will design a multi-agent system and polish it through an optimization algorithm. When the multi-agent system is deployed, the finite state machine will control the agent's actions and the state transitions. To evaluate our framework, we conduct experiments on both text-based tasks and practical tasks. The results indicate that the generated multi-agent system surpasses other auto-designed methods and can achieve a comparable performance with the human-designed multi-agent system, which is optimized for those specific tasks.
|
LLM Agent, Multi-Agent System
|
We introduce a new framework that can automatically produce multi-agent systems base on finite state machines
| 13,114 | null |
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] | null |
547 |
RULEBREAKERS: Challenging LLMs at the Crossroads between Formal Logic and Human-like Reasoning
|
https://openreview.net/forum?id=uqpML2nbIz
|
[
"Jason Chan",
"Robert J. Gaizauskas",
"Zhixue Zhao"
] |
Poster
|
Formal logic enables computers to reason in natural language by representing sentences in symbolic forms and applying rules to derive conclusions. However, in what our study characterizes as "rulebreaker" scenarios, this method can lead to conclusions that are typically not inferred or accepted by humans given their common sense and factual knowledge. Inspired by works in cognitive science, we create RULEBREAKERS, the first dataset for rigorously evaluating the ability of large language models (LLMs) to recognize and respond to rulebreakers (versus non-rulebreakers) in a knowledge-informed and human-like manner. Evaluating seven LLMs, we find that most models achieve mediocre accuracy on RULEBREAKERS and exhibit some tendency to over-rigidly apply logical rules, unlike what is expected from typical human reasoners. Further analysis suggests that this apparent failure is potentially associated with the models' poor utilization of their world knowledge and their attention distribution patterns. Whilst revealing a limitation of current LLMs, our study also provides a timely counterbalance to a growing body of recent works that propose methods relying on formal logic to improve LLMs' general reasoning capabilities, highlighting their risk of further increasing divergence between LLMs and human-like reasoning.
|
reasoning, logic, human-like reasoning, cognitive science, large language models
| null | 13,113 |
2410.16502
|
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] | null |
548 |
Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation
|
https://openreview.net/forum?id=cZi1njoRT6
|
[
"Sadegh Mahdavi",
"Muchen Li",
"Kaiwen Liu",
"Christos Thrampoulidis",
"Leonid Sigal",
"Renjie Liao"
] |
Poster
|
Advances in Large Language Models (LLMs) have sparked interest in their ability to solve Olympiad-level math problems.
However, the training and evaluation of these models are constrained by the limited size and quality of available datasets, as creating large-scale data for such advanced problems requires extensive effort from human experts.
In addition, current benchmarks are prone to contamination, leading to unreliable evaluations.
In this paper, we present an automated pipeline that leverages the rich resources of the Art of Problem Solving (AoPS) forum, which predominantly features Olympiad-level problems and community-driven solutions.
Using open-source LLMs, we develop a method to extract question-answer pairs from the forum, resulting in **AoPS-Instruct**, a dataset of more than 600,000 high-quality QA pairs.
Our experiments demonstrate that fine-tuning LLMs on AoPS-Instruct improves their reasoning abilities across various benchmarks.
Moreover, we build an automatic pipeline that introduces **LiveAoPSBench**, an evolving evaluation set with timestamps, derived from the latest forum data, providing a contamination-resistant benchmark for assessing LLM performance.
Notably, we observe a significant decline in LLM performance over time, suggesting their success on older examples may stem from pre-training exposure rather than true reasoning ability.
Our work presents a scalable approach to creating and maintaining large-scale, high-quality datasets for advanced math reasoning, offering valuable insights into the capabilities and limitations of LLMs in this domain.
|
Mathematical Reasoning, Large Language Models, Evaluation
|
We propose a new dataset and benchmark for training and evaluating mathematical reasoning in LLMs.
| 13,108 |
2501.14275
|
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] |
https://github.com/DSL-Lab/aops
|
549 |
CRANE: Reasoning with constrained LLM generation
|
https://openreview.net/forum?id=wKs9fHYxCV
|
[
"Debangshu Banerjee",
"Tarun Suresh",
"Shubham Ugare",
"Sasa Misailovic",
"Gagandeep Singh"
] |
Poster
|
Code generation, symbolic math reasoning, and other tasks require LLMs to produce outputs that are both syntactically and semantically correct. Constrained LLM generation is a promising direction to enforce adherence to formal grammar, but prior works have empirically observed that strict enforcement of formal constraints often diminishes the reasoning capabilities of LLMs. In this work, we first provide a theoretical explanation for why constraining LLM outputs to very restrictive grammars that only allow syntactically valid final answers reduces the reasoning capabilities of the model. Second, we demonstrate that by augmenting the output grammar with carefully designed additional rules, it is always possible to preserve the reasoning capabilities of the LLM while ensuring syntactic and semantic correctness in its outputs. Building on these theoretical insights, we propose a reasoning-augmented constrained decoding algorithm, CRANE, which effectively balances the correctness of constrained generation with the flexibility of unconstrained generation.
Experiments on multiple open-source LLMs and benchmarks show that CRANE significantly outperforms both state-of-the-art
constrained decoding strategies and standard unconstrained decoding, showing up to 10% points accuracy improvement over baselines on challenging symbolic reasoning benchmarks GSM-symbolic and FOLIO.
|
LLM reasoning, Constrained Decoding, Grammar Guided Generation.
|
A constrained decoding algorithm that combines reasoning capabilities with structural correctness.
| 13,104 |
2502.09061
|
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] | null |
550 |
sciLaMA: A Single-Cell Representation Learning Framework to Leverage Prior Knowledge from Large Language Models
|
https://openreview.net/forum?id=0m4VsLwj5s
|
[
"Hongru Hu",
"Shuwen Zhang",
"Yongin Choi",
"Venkat S. Malladi",
"Gerald Quon"
] |
Poster
|
Single-cell RNA sequencing (scRNA-seq) enables high-resolution exploration of cellular diversity and gene regulation, yet analyzing such data remains challenging due to technical and methodological limitations. Existing task-specific deep generative models like Variational Auto-Encoder (VAE) and its variants struggle to incorporate external biological knowledge, while transformer-based foundational large Language Models (LLMs or large LaMs) face limitations in computational cost and applicability to tabular gene expression data. Here, we introduce sciLaMA (single-cell interpretable Language Model Adapter), a novel representation learning framework that bridges these gaps by integrating static gene embeddings from multimodal LaMs with scRNA-seq tabular data through a paired-VAE architecture. Our approach generates context-aware representations for both cells and genes and outperforms state-of-the-art methods in key single-cell downstream tasks, including batch effect correction, cell clustering, and cell-state-specific gene marker and module identification, while maintaining computational efficiency. sciLaMA offers a computationally efficient, unified framework for comprehensive single-cell data analysis and biologically interpretable gene module discovery.
|
Representation Learning, Large Language Model, Batch Effect Correction, Gene Module Discovery
| null | 13,101 | null |
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] |
https://github.com/microsoft/sciLaMA
|
551 |
Domain-Adapted Diffusion Model for PROTAC Linker Design Through the Lens of Density Ratio in Chemical Space
|
https://openreview.net/forum?id=jkyUbkNJyH
|
[
"Zixing Song",
"Ziqiao Meng",
"José Miguel Hernández-Lobato"
] |
Poster
|
Proteolysis-targeting chimeras (PROTACs) are a groundbreaking technology for targeted protein degradation, but designing effective linkers that connect two molecular fragments to form a drug-candidate PROTAC molecule remains a key challenge. While diffusion models show promise in molecular generation, current diffusion models for PROTAC linker design are typically trained on small molecule datasets, introducing distribution mismatches in the chemical space between small molecules and target PROTACs. Direct fine-tuning on limited PROTAC datasets often results in overfitting and poor generalization. In this work, we propose DAD-PROTAC, a domain-adapted diffusion model for PROTAC linker design, which addresses this distribution mismatch in chemical space through density ratio estimation to bridge the gap between small-molecule and PROTAC domains. By decomposing the target score estimator into a pre-trained score function and a lightweight score correction term, DAD-PROTAC achieves efficient fine-tuning without full retraining. Experimental results demonstrate its superior ability to generate high-quality PROTAC linkers.
|
PROTAC, Linker Design, Diffusion Models
|
We present a domain-adapted diffusion model for PROTAC linker design task.
| 13,097 | null |
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] | null |
552 |
Leveraging Predictive Equivalence in Decision Trees
|
https://openreview.net/forum?id=CdqBQwFG9i
|
[
"Hayden McTavish",
"Zachery Boner",
"Jon Donnelly",
"Margo Seltzer",
"Cynthia Rudin"
] |
Poster
|
Decision trees are widely used for interpretable machine learning due to their clearly structured reasoning process. However, this structure belies a challenge we refer to as predictive equivalence: a given tree's decision boundary can be represented by many different decision trees. The presence of models with identical decision boundaries but different evaluation processes makes model selection challenging. The models will have different variable importance and behave differently in the presence of missing values, but most optimization procedures will arbitrarily choose one such model to return. We present a boolean logical representation of decision trees that does not exhibit predictive equivalence and is faithful to the underlying decision boundary. We apply our representation to several downstream machine learning tasks. Using our representation, we show that decision trees are surprisingly robust to test-time missingness of feature values; we address predictive equivalence's impact on quantifying variable importance; and we present an algorithm to optimize the cost of reaching predictions.
|
Decision Tree, Interpretability, Machine Learning, Missing Data, Variable Importance, Cost Optimization
|
Decision trees’ representations are not unique; we use this to improve missingness handling, variable importance, and cost optimization.
| 13,085 |
2506.14143
|
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] |
https://github.com/HaydenMcT/predictive-equivalence
|
553 |
Flexibility-conditioned protein structure design with flow matching
|
https://openreview.net/forum?id=890gHX7ieS
|
[
"Vsevolod Viliuga",
"Leif Seute",
"Nicolas Wolf",
"Simon Wagner",
"Arne Elofsson",
"Jan Stühmer",
"Frauke Gräter"
] |
Poster
|
Recent advances in geometric deep learning and generative modeling have enabled the design of novel proteins with a wide range of desired properties. However, current state-of-the-art approaches are typically restricted to generating proteins with only static target properties, such as motifs and symmetries. In this work, we take a step towards overcoming this limitation by proposing a framework to condition structure generation on flexibility, which is crucial for key functionalities such as catalysis or molecular recognition. We first introduce BackFlip, an equivariant neural network for predicting per-residue flexibility from an input backbone structure. Relying on BackFlip, we propose FliPS, an SE(3)-equivariant conditional flow matching model that solves the inverse problem, that is, generating backbones that display a target flexibility profile. In our experiments, we show that FliPS is able to generate novel and diverse protein backbones with the desired flexibility, verified by Molecular Dynamics (MD) simulations.
|
Proteins, Protein design, Molecular Dynamics, Flow Matching, Conditional Flow Matching, Generative models, Equivariant models
|
We propose a flow-based generative model for conditioning protein structure design on per-residue flexibility
| 13,075 | null |
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] |
https://github.com/graeter-group/flips
|
554 |
Local Manifold Approximation and Projection for Manifold-Aware Diffusion Planning
|
https://openreview.net/forum?id=EHG5Iv1mmb
|
[
"Kyowoon Lee",
"Jaesik Choi"
] |
Poster
|
Recent advances in diffusion-based generative modeling have demonstrated significant promise in tackling long-horizon, sparse-reward tasks by leveraging offline datasets. While these approaches have achieved promising results, their reliability remains inconsistent due to the inherent stochastic risk of producing infeasible trajectories, limiting their applicability in safety-critical applications. We identify that the primary cause of these failures is inaccurate guidance during the sampling procedure, and demonstrate the existence of manifold deviation by deriving a lower bound on the guidance gap. To address this challenge, we propose *Local Manifold Approximation and Projection* (LoMAP), a *training-free* method that projects the guided sample onto a low-rank subspace approximated from offline datasets, preventing infeasible trajectory generation. We validate our approach on standard offline reinforcement learning benchmarks that involve challenging long-horizon planning. Furthermore, we show that, as a standalone module, LoMAP can be incorporated into the hierarchical diffusion planner, providing further performance enhancements.
|
Offline Reinforcement Learning, Trajectory Optimization, Diffusion Models, Sequential Decision Making
|
training-free method that corrects off-manifold deviations in diffusion-based planners
| 13,073 |
2506.00867
|
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] |
https://github.com/leekwoon/lomap
|
555 |
Spatial Reasoning with Denoising Models
|
https://openreview.net/forum?id=dWuN4jCQo3
|
[
"Christopher Wewer",
"Bartlomiej Pogodzinski",
"Bernt Schiele",
"Jan Eric Lenssen"
] |
Poster
|
We introduce Spatial Reasoning Models (SRMs), a framework to perform reasoning over sets of continuous variables via denoising generative models. SRMs infer continuous representations on a set of unobserved variables, given observations on observed variables.
Current generative models on spatial domains, such as diffusion and flow matching models, often collapse to hallucination in case of complex distributions. To measure this, we introduce a set of benchmark tasks that test the quality of complex reasoning in generative models and can quantify hallucination. The SRM framework allows to report key findings about importance of sequentialization in generation, the associated order, as well as the sampling strategies during training. It demonstrates, for the first time, that order of generation can successfully be predicted by the denoising network itself. Using these findings, we can increase the accuracy of specific reasoning tasks from <1% to >50%. Our [project website](https://geometric-rl.mpi-inf.mpg.de/srm/) provides additional videos, code, and the benchmark datasets.
|
Generative Models, Reasoning, Image Generation
|
A diffusion-based framework for structured reasoning over continuous spatial variables — enabling flexible sampling strategies w.r.t. amount and order of sequentialization. Introduces a benchmark to measure hallucination.
| 13,053 |
2502.21075
|
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] |
https://github.com/Chrixtar/SRM
|
556 |
TruthFlow: Truthful LLM Generation via Representation Flow Correction
|
https://openreview.net/forum?id=7TDnfx5s14
|
[
"Hanyu Wang",
"Bochuan Cao",
"Yuanpu Cao",
"Jinghui Chen"
] |
Poster
|
Large language models (LLMs) are known to struggle with consistently generating truthful responses. While various representation intervention techniques have been proposed, these methods typically apply a universal representation correction vector to all input queries, limiting their effectiveness against diverse queries in practice. In this study, we introduce TruthFlow, a novel method that leverages the Flow Matching technique for query-specific truthful representation correction. Specifically, TruthFlow first uses a flow model to learn query-specific correction vectors that transition representations from hallucinated to truthful states. Then, during inference, the trained flow model generates these correction vectors to enhance the truthfulness of LLM outputs. Experimental results demonstrate that TruthFlow significantly improves performance on open-ended generation tasks across various advanced LLMs evaluated on TruthfulQA. Moreover, the trained TruthFlow model exhibits strong transferability, performing effectively on other unseen hallucination benchmarks.
|
Large Language Models, Hallucination, Representation Intervention, Flow Matching
| null | 13,040 |
2502.04556
|
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] | null |
557 |
DCBM: Data-Efficient Visual Concept Bottleneck Models
|
https://openreview.net/forum?id=BdO4R6XxUH
|
[
"Katharina Prasse",
"Patrick Knab",
"Sascha Marton",
"Christian Bartelt",
"Margret Keuper"
] |
Poster
|
Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts. However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data-sparse scenarios. We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability. DCBMs define concepts as image regions detected by segmentation or detection foundation models, allowing each image to generate multiple concepts across different granularities. Exclusively containing dataset-specific concepts, DCBMs are well suited for fine-grained classification and out-of-distribution tasks. Attribution analysis using Grad-CAM demonstrates that DCBMs deliver visual concepts that can be localized in test images. By leveraging dataset-specific concepts instead
of predefined or general ones, DCBMs enhance adaptability to new domains. The code is available at: https://github.com/KathPra/DCBM.
|
Explainable AI, concept bottleneck models, foundation models
|
Data-efficient CBMs (DCBMs) generate concepts from image regions detected by segmentation or detection models.
| 13,036 |
2412.11576
|
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] |
https://github.com/KathPra/DCBM
|
558 |
SPEX: Scaling Feature Interaction Explanations for LLMs
|
https://openreview.net/forum?id=pRlKbAwczl
|
[
"Justin Singh Kang",
"Landon Butler",
"Abhineet Agarwal",
"Yigit Efe Erginbas",
"Ramtin Pedarsani",
"Bin Yu",
"Kannan Ramchandran"
] |
Poster
|
Large language models (LLMs) have revolutionized machine learning due to their ability to capture complex interactions between input features. Popular post-hoc explanation methods like SHAP provide *marginal* feature attributions, while their extensions to interaction importances only scale to small input lengths ($\approx 20$). We propose *Spectral Explainer* (SPEX), a model-agnostic interaction attribution algorithm that efficiently scales to large input lengths ($\approx 1000)$. SPEX exploits underlying natural sparsity among interactions—common in real-world data—and applies a sparse Fourier transform using a channel decoding algorithm to efficiently identify important interactions. We perform experiments across three difficult long-context datasets that require LLMs to utilize interactions between inputs to complete the task. For large inputs, SPEX outperforms marginal attribution methods by up to 20\% in terms of faithfully reconstructing LLM outputs. Further, SPEX successfully identifies key features and interactions that strongly influence model output. For one of our datasets, *HotpotQA*, SPEX provides interactions that align with human annotations. Finally, we use our model-agnostic approach to generate explanations to demonstrate abstract reasoning in closed-source LLMs (*GPT-4o mini*) and compositional reasoning in vision-language models.
|
Interpretability, Signal Processing, Compressed Sensing, SHAP, Interactions
|
Enabling post-hoc interaction explanations with orders of magnitude larger input spaces over the previous state of the art via a signal processing inspired algorithm.
| 13,030 |
2502.13870
|
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] |
https://github.com/basics-lab/spectral-explain
|
559 |
S4S: Solving for a Fast Diffusion Model Solver
|
https://openreview.net/forum?id=9OaZCNbV2w
|
[
"Eric Frankel",
"Sitan Chen",
"Jerry Li",
"Pang Wei Koh",
"Lillian J. Ratliff",
"Sewoong Oh"
] |
Poster
|
Diffusion models (DMs) create samples from a data distribution by starting from random noise and iteratively solving a reverse-time ordinary differential equation (ODE).
Because each step in the iterative solution requires an expensive neural function evaluation (NFE), there has been significant interest in approximately solving these diffusion ODEs with only a few NFEs without modifying the underlying model.
However, in the few NFE regime, we observe that tracking the true ODE evolution is fundamentally impossible using traditional ODE solvers.
In this work, we propose a new method that learns a good solver for the DM, which we call **S**olving **for** the **S**olver (**S4S**).
S4S directly optimizes a solver to obtain good generation quality by learning to match the output of a strong teacher solver.
We evaluate S4S on six different pre-trained DMs, including pixel-space and latent-space DMs for both conditional and unconditional sampling.
In all settings, S4S uniformly improves the sample quality relative to traditional ODE solvers.
Moreover, our method is lightweight, data-free, and can be plugged in black-box on top of any discretization schedule or architecture to improve performance.
Building on top of this, we also propose **S4S-Alt**, which optimizes both the solver and the discretization schedule.
By exploiting the full design space of DM solvers, with 5 NFEs, we achieve an FID of 3.73 on CIFAR10 and 13.26 on MS-COCO, representing a $1.5\times$ improvement over previous training-free ODE methods.
|
Diffusion models, efficient sampling, ODE solvers
|
We learn the coefficients and time steps of diffusion model solvers to minimize global approximation error.
| 13,027 | null |
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] | null |
560 |
Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders
|
https://openreview.net/forum?id=jYmGi1175R
|
[
"Rogelio A. Mancisidor",
"Robert Jenssen",
"Shujian Yu",
"Michael Kampffmeyer"
] |
Poster
|
Multimodal learning with variational autoencoders (VAEs) requires estimating joint distributions to evaluate the evidence lower bound (ELBO). Current methods, the product and mixture of experts, aggregate single-modality distributions assuming independence for simplicity, which is an overoptimistic assumption. This research introduces a novel methodology for aggregating single-modality distributions by exploiting the principle of *consensus of dependent experts* (CoDE), which circumvents the aforementioned assumption. Utilizing the CoDE method, we propose a novel ELBO that approximates the joint likelihood of the multimodal data by learning the contribution of each subset of modalities. The resulting CoDE-VAE model demonstrates better performance in terms of balancing the trade-off between generative coherence and generative quality, as well as generating more precise log-likelihood estimations. CoDE-VAE further minimizes the generative quality gap as the number of modalities increases. In certain cases, it reaches a generative quality similar to that of unimodal VAEs, which is a desirable property that is lacking in most current methods. Finally, the classification accuracy achieved by CoDE-VAE is comparable to that of state-of-the-art multimodal VAE models.
|
Multimodal Learning, Product of Experts, Generative Models, Variational Autoencoders
|
Multimodal learning with VAEs where the dependence between expert distributions is taken into account.
| 13,017 |
2505.01134
|
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] |
https://github.com/rogelioamancisidor/codevae
|
561 |
TopInG: Topologically Interpretable Graph Learning via Persistent Rationale Filtration
|
https://openreview.net/forum?id=u4LlYWJHUF
|
[
"Cheng Xin",
"Fan Xu",
"Xin Ding",
"Jie Gao",
"Jiaxin Ding"
] |
Poster
|
Graph Neural Networks (GNNs) have shown remarkable success across various scientific fields,
yet their adoption in critical decision-making is often hindered by a lack of interpretability. Recently,
intrinsic interpretable GNNs have been studied to provide insights into model predictions by identifying rationale substructures in graphs. However, existing methods face challenges when the underlying rationale subgraphs are complex and varied. In this work, we propose TopInG: Topologically Interpretable Graph Learning, a novel topological framework that leverages persistent homology to identify persistent rationale subgraphs. TopInG employs a rationale filtration learning approach to model an autoregressive generating process of rationale subgraphs, and introduces a self-adjusted topological constraint, termed topological discrepancy, to enforce a persistent topological distinction between rationale subgraphs and irrelevant counterparts. We provide theoretical guarantees that our loss function is uniquely optimized by the ground truth under specific conditions. Extensive experiments demonstrate TopInG's effectiveness in tackling key challenges, such as handling variform rationale subgraphs, balancing predictive performance with interpretability, and mitigating spurious correlations. Results show that our approach improves upon state-of-the-art
methods on both predictive accuracy and interpretation quality.
|
XAI, XGNN, Graph Representation, Graph Neural Networks, Topological Data Analysis, TDA, Persistent Homology, Interpretable GNN, Explainable GNN, Topological Discrepancy
|
A topologically interpretable GNN with a novel topological discrepancy loss is proved to be uniquely optimized by ground truth.
| 13,016 | null |
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] |
https://github.com/unlimitedshaver/TopoEx
|
562 |
Principal-Agent Bandit Games with Self-Interested and Exploratory Learning Agents
|
https://openreview.net/forum?id=yI24Wy5YaN
|
[
"Junyan Liu",
"Lillian J. Ratliff"
] |
Poster
|
We study the repeated principal-agent bandit game, where the principal indirectly explores an unknown environment by incentivizing an agent to play arms.
Unlike prior work that assumes a greedy agent with full knowledge of reward means, we consider a self-interested learning agent who iteratively updates reward estimates and may explore arbitrarily with some probability.
As a warm-up, we first consider a self-interested learning agent without exploration.
We propose algorithms for both i.i.d. and linear reward settings with bandit feedback in a finite horizon $T$, achieving regret bounds of $\widetilde{O}(\sqrt{T})$ and $\widetilde{O}(T^{\frac{2}{3}})$, respectively.
Specifically, these algorithms rely on a novel elimination framework coupled with new search algorithms which accommodate the uncertainty from the agent's learning behavior. We then extend the framework to handle an exploratory learning agent and develop an algorithm to achieve a $\widetilde{O}(T^{\frac{2}{3}})$ regret bound in i.i.d. reward setup by enhancing the robustness of our elimination framework to the potential agent exploration. Finally, when our agent model reduces to that in (Dogan et al., 2023a), we propose an algorithm based on our robust framework, which achieves a $\widetilde{O}(\sqrt{T})$ regret bound, significantly improving upon their $\widetilde{O}(T^{\frac{11}{12}})$ bound.
|
Bandits, Principal-agent problem, Incentive design, Regret minimization
| null | 13,012 |
2412.16318
|
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] | null |
563 |
Language Models over Canonical Byte-Pair Encodings
|
https://openreview.net/forum?id=eCVrfVDNSY
|
[
"Tim Vieira",
"Tianyu Liu",
"Clemente Pasti",
"Yahya Emara",
"Brian DuSell",
"Benjamin LeBrun",
"Mario Giulianelli",
"Juan Luis Gastaldi",
"Timothy J. O'Donnell",
"Ryan Cotterell"
] |
Poster
|
Modern language models represent probability distributions over character strings as distributions over (shorter) token strings derived via a deterministic tokenizer, such as byte-pair encoding. While this approach is highly effective at scaling up language models to large corpora, its current incarnations have a concerning property: the model assigns nonzero probability mass to an exponential number of *noncanonical* token encodings of each character string—these are token strings that decode to valid character strings but are impossible under the deterministic tokenizer (i.e., they will never be seen in any training corpus, no matter how large). This misallocation is both erroneous, as noncanonical strings never appear in training data, and wasteful, diverting probability mass away from plausible outputs. These are avoidable mistakes! In this work, we propose methods to enforce canonicality in token-level language models, ensuring that only canonical token strings are assigned positive probability. We present two approaches: (1) canonicality by conditioning, leveraging test-time inference strategies without additional training, and (2) canonicality by construction, a model parameterization that guarantees canonical outputs but requires training. We demonstrate that fixing canonicality mistakes improves the likelihood of held-out data for several models and corpora.
|
tokenization, probabilistic inference, structured prediction, constrained generation
|
Tokenized language model place nontrival probability on invalid, noncanonical encodings; fixing these mistakes with our new methods improves helps!
| 13,011 |
2506.07956
|
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] |
https://github.com/genlm/canonical-icml-2025
|
564 |
Provable Zero-Shot Generalization in Offline Reinforcement Learning
|
https://openreview.net/forum?id=1jx6bgemqg
|
[
"Zhiyong Wang",
"Chen Yang",
"John C.S. Lui",
"Dongruo Zhou"
] |
Poster
|
In this work, we study offline reinforcement learning (RL) with zero-shot generalization property (ZSG), where the agent has access to an offline dataset including experiences from different environments, and the goal of the agent is to train a policy over the training environments which performs well on test environments without further interaction. Existing work showed that classical offline RL fails to generalize to new, unseen environments. We propose pessimistic empirical risk minimization (PERM) and pessimistic proximal policy optimization (PPPO), which leverage pessimistic policy evaluation to guide policy learning and enhance generalization. We show that both PERM and PPPO are capable of finding a near-optimal policy with ZSG. Our result serves as a first step in understanding the foundation of the generalization phenomenon in offline reinforcement learning.
|
offline reinforcement learning, generalization
| null | 13,001 |
2503.07988
|
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] | null |
565 |
Speculative Prefill: Turbocharging TTFT with Lightweight and Training-Free Token Importance Estimation
|
https://openreview.net/forum?id=bzbuZ0ItBq
|
[
"Jingyu Liu",
"Beidi Chen",
"Ce Zhang"
] |
Poster
|
Improving time-to-first-token (TTFT) is an essentially important objective in modern large language model (LLM) inference engines. Optimizing TTFT directly results in higher maximal QPS and meets the requirements of many critical applications. However, boosting TTFT is notoriously challenging since it is compute-bounded and the performance bottleneck shifts from the self-attention that many prior works focus on to the MLP part. In this work, we present SpecPrefill, a training free framework that accelerates the inference TTFT for both long and medium context queries based on the following insight: LLMs are generalized enough to preserve the quality given only a carefully chosen subset of prompt tokens. At its core, SpecPrefill leverages a lightweight model to speculate locally important tokens based on the context. These tokens, along with the necessary positional information, are then sent to the main model for processing. We evaluate SpecPrefill with a diverse set of tasks, followed by a comprehensive benchmarking of performance improvement both in a real end-to-end setting and ablation studies. SpecPrefill manages to serve Llama-3.1-405B-Instruct-FP8 with up to 7$\times$ maximal end-to-end QPS on real downstream tasks and 7.66$\times$ TTFT improvement.
|
Inference Acceleration, TTFT, Large Language Model, Efficient Inference
| null | 12,989 |
2502.02789
|
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] |
https://github.com/Jingyu6/speculative_prefill
|
566 |
Improved Learning via k-DTW: A Novel Dissimilarity Measure for Curves
|
https://openreview.net/forum?id=VCjPjexvpM
|
[
"Amer Krivošija",
"Alexander Munteanu",
"André Nusser",
"Chris Schwiegelshohn"
] |
Poster
|
This paper introduces $k$-Dynamic Time Warping ($k$-DTW), a novel dissimilarity measure for polygonal curves. $k$-DTW has stronger metric properties than Dynamic Time Warping (DTW) and is more robust to outliers than the Fréchet distance, which are the two gold standards of dissimilarity measures for polygonal curves. We show interesting properties of $k$-DTW and give an exact algorithm as well as a $(1+\varepsilon)$-approximation algorithm for $k$-DTW by a parametric search for the $k$-th largest matched distance. We prove the first dimension-free learning bounds for curves and further learning theoretic results. $k$-DTW not only admits smaller sample size than DTW for the problem of learning the median of curves, where some factors depending on the curves' complexity $m$ are replaced by $k$, but we also show a surprising separation on the associated Rademacher and Gaussian complexities: $k$-DTW admits strictly smaller bounds than DTW, by a factor $\tilde\Omega(\sqrt{m})$ when $k\ll m$. We complement our theoretical findings with an experimental illustration of the benefits of using $k$-DTW for clustering and nearest neighbor classification.
|
polygonal curves, dynamic time warping, learning theory, clustering, classification
|
We introduce $k$-DTW, a novel distance for polygonal curves that generalizes and provides the best of the gold standards: DTW and Fréchet distances.
| 12,988 | null |
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] |
https://github.com/akrivosija/kDTW
|
567 |
Multi-Marginal Stochastic Flow Matching for High-Dimensional Snapshot Data at Irregular Time Points
|
https://openreview.net/forum?id=ZLyb8DwXXE
|
[
"Justin Lee",
"Behnaz Moradijamei",
"Heman Shakeri"
] |
Poster
|
Modeling the evolution of high-dimensional systems from limited snapshot observations at irregular time points poses a significant challenge in quantitative biology and related fields. Traditional approaches often rely on dimensionality reduction techniques, which can oversimplify the dynamics and fail to capture critical transient behaviors in non-equilibrium systems. We present Multi-Marginal Stochastic Flow Matching (MMSFM), a novel extension of simulation-free score and flow matching methods to the multi-marginal setting, enabling the alignment of high-dimensional data measured at non-equidistant time points without reducing dimensionality. The use of measure-valued splines enhances robustness to irregular snapshot timing, and score matching prevents overfitting in high-dimensional spaces. We validate our framework on several synthetic and benchmark datasets and apply it to single-cell perturbation data from melanoma cell lines and gene expression data collected at uneven time points.
|
Stochastic Flow Matching, Multi-Marginal Optimal Transport, Irregular Time Points, Snapshot Data, Simulation-Free Methods, Score Matching, Generative Models, Single-Cell Data
|
Modeling the evolution of high-dimensional systems from irregular timed sampled snapshots using overlapping windows of marginal distributions and measure-valued splines.
| 12,986 | null |
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] | null |
568 |
EEG-Language Pretraining for Highly Label-Efficient Clinical Phenotyping
|
https://openreview.net/forum?id=yaI2ZYFmeD
|
[
"Sam Gijsen",
"Kerstin Ritter"
] |
Poster
|
Multimodal language modeling has enabled breakthroughs for representation learning, yet remains unexplored in the realm of functional brain data for clinical phenotyping. This paper pioneers EEG-language models (ELMs) trained on clinical reports and 15000 EEGs. We propose to combine multimodal alignment in this novel domain with timeseries cropping and text segmentation, enabling an extension based on multiple instance learning to alleviate misalignment between irrelevant EEG or text segments. Our multimodal models significantly improve over EEG-only models across four clinical evaluations and for the first time enable zero-shot classification as well as retrieval of both neural signals and reports. In sum, these results highlight the potential of ELMs, representing significant progress for clinical applications.
|
multimodal, neuroscience, eeg, medical, pretraining, representation learning
|
First application of multimodal language pretraining to the domain of medical EEG to improve clinical phenotyping.
| 12,985 | null |
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] |
https://github.com/SamGijsen/ELM
|
569 |
DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applications
|
https://openreview.net/forum?id=JXCiQteuOv
|
[
"Ibrahim Fayad",
"Max Zimmer",
"Martin Schwartz",
"Fabian Gieseke",
"Philippe CIAIS",
"Gabriel Belouze",
"Sarah Brood",
"Aurélien de Truchis",
"Alexandre d'Aspremont"
] |
Poster
|
Significant efforts have been directed towards adapting self-supervised multimodal learning for Earth observation applications. However, most current methods produce coarse patch-sized embeddings, limiting their effectiveness and integration with other modalities like LiDAR. To close this gap, we present DUNIA, an approach to learn pixel-sized embeddings through cross-modal alignment between images and full-waveform LiDAR data. As the model is trained in a contrastive manner, the embeddings can be directly leveraged in the context of a variety of environmental monitoring tasks in a zero-shot setting. In our experiments, we demonstrate the effectiveness of the embeddings for seven such tasks: canopy height mapping, fractional canopy cover, land cover mapping, tree species identification, plant area index, crop type classification, and per-pixel waveform-based vertical structure mapping. The results show that the embeddings, along with zero-shot classifiers, often outperform specialized supervised models, even in low-data regimes. In the fine-tuning setting, we show strong performances near or better than the state-of-the-art on five out of six tasks.
|
earth observation, multi-modality, self-supervised learning, cross-modal retrieval
| null | 12,982 |
2502.17066
|
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] |
https://github.com/AI4Forest/DUNIA
|
570 |
AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs
|
https://openreview.net/forum?id=dqcl5SNbc8
|
[
"Anselm Paulus",
"Arman Zharmagambetov",
"Chuan Guo",
"Brandon Amos",
"Yuandong Tian"
] |
Poster
|
Large Language Models (LLMs) are vulnerable to **jailbreaking attacks** that lead to generation of inappropriate or harmful content.
Manual red-teaming requires a time-consuming search for adversarial prompts, whereas automatic adversarial prompt generation often leads to semantically meaningless attacks that do not scale well.
In this paper, we present a novel method that uses another LLM, called **AdvPrompter**, to generate human-readable adversarial prompts in seconds.
AdvPrompter, which is trained using an alternating optimization algorithm, generates suffixes that veil the input instruction without changing its meaning, such that the TargetLLM is lured to give a harmful response.
Experimental results on popular open source TargetLLM show highly competitive results on the AdvBench and HarmBench datasets, that also transfer to closed-source black-box LLMs.
We also show that training on adversarial suffixes generated by AdvPrompter is a promising strategy for improving the robustness of LLMs to jailbreaking attacks.
|
adversarial attacks, prompt optimization, red-teaming LLMs
|
A novel method for generating human-readable adversarial prompts in seconds for attacking and red-teaming LLMs.
| 12,977 |
2404.16873
|
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] |
https://github.com/facebookresearch/advprompter
|
571 |
Optimizing Test-Time Compute via Meta Reinforcement Finetuning
|
https://openreview.net/forum?id=TqODUDsU4u
|
[
"Yuxiao Qu",
"Matthew Y. R. Yang",
"Amrith Setlur",
"Lewis Tunstall",
"Edward Emanuel Beeching",
"Ruslan Salakhutdinov",
"Aviral Kumar"
] |
Poster
|
Training models to efficiently use test-time compute is crucial for improving the reasoning performance of LLMs. While current methods mostly do so via fine-tuning on search traces or running RL against the 0/1 outcome reward, do these approaches efficiently utilize test-time compute? Would these approaches continue to scale as the budget improves? In this paper, we try to answer these questions. We formalize the problem of optimizing test-time compute as a meta reinforcement learning (RL) problem, which provides a principled perspective on spending test-time compute from the lens of exploration and exploitation. It also motivates the use of cumulative regret to measure the efficacy of test-time compute by viewing a long output stream as consisting of several episodes from the model. While current state-of-the-art models do not optimize regret, we show that regret can be minimized by running final 0/1 reward RL regularized by a dense reward bonus, given by the "information gain" from each subsequent block in the output stream. We prescribe an approach for quantifying information gain, which measures the utility of an intermediate segment of tokens towards improving accuracy of the final answer. We instantiate this idea to develop MRT, a new class of finetuning methods for optimizing test-time compute. Fine-tuning with MRT leads to substantial improvements in both performance and token efficiency on the AIME dataset.
|
meta-RL, test-time compute, information gain, self-correction, LLM optimization, cumulative regret, backtracking
|
This paper introduces a new paradigm to optimize test-time compute in LLMs
| 12,976 | null |
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] |
https://github.com/CMU-AIRe/MRT
|
572 |
Potemkin Understanding in Large Language Models
|
https://openreview.net/forum?id=oetxkccLoq
|
[
"Marina Mancoridis",
"Bec Weeks",
"Keyon Vafa",
"Sendhil Mullainathan"
] |
Poster
|
Large language models (LLMs) are regularly evaluated using benchmark datasets. But what justifies making inferences about an LLM's capabilities based on its answers to a curated set of questions? This paper first introduces a formal framework to address this question. The key is to note that the benchmarks used to test LLMs---such as AP exams---are also those used to test people. However, this raises an implication: such benchmarks are only valid tests if LLMs misunderstand concepts in ways that mirror human misunderstandings. Otherwise, success on benchmarks only demonstrates **potemkin understanding:** the illusion of understanding driven by answers irreconcilable with how any human would interpret a concept. We present two procedures for quantifying the existence of potemkins: one using a specially designed benchmark in three domains, the other using a general procedure that provides a lower-bound on their prevalence. We find that potemkins are ubiquitous across models, tasks, and domains. We also find that these failures reflect not just incorrect understanding, but deeper internal incoherence in concept representations.
|
evaluation, benchmark, large language models, potemkin understanding
|
We find that large language models exhibit a category of failure in conceptual comprehension called potemkin understanding.
| 12,975 |
2506.21521
|
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] |
https://github.com/MarinaMancoridis/PotemkinBenchmark
|
573 |
EncryptedLLM: Privacy-Preserving Large Language Model Inference via GPU-Accelerated Fully Homomorphic Encryption
|
https://openreview.net/forum?id=PGNff6H1TV
|
[
"Leo de Castro",
"Daniel Escudero",
"Adya Agrawal",
"Antigoni Polychroniadou",
"Manuela Veloso"
] |
Poster
|
As large language models (LLMs) become more powerful, the computation required to run these models is increasingly outsourced to a third-party cloud. While this saves clients' computation, it risks leaking the clients' LLM queries to the cloud provider. Fully homomorphic encryption (FHE) presents a natural solution to this problem: simply encrypt the query and evaluate the LLM homomorphically on the cloud machine. The result remains encrypted and can only be learned by the client who holds the secret key. In this work, we present a GPU-accelerated implementation of FHE and use this implementation to benchmark an encrypted GPT-2 forward pass, with runtimes over $200\times$ faster than the CPU baseline. We also present novel and extensive experimental analysis of approximations of LLM activation functions to maintain accuracy while achieving this performance.
|
Fully homomorphic encryption, encrypted inference, approximate activation functions
|
We develop a GPU-accelerated implementation of FHE and use this implementation to evaluate GPT-2.
| 12,971 | null |
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] | null |
574 |
Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations
|
https://openreview.net/forum?id=fGbGF0kl5L
|
[
"Jason Qin",
"Hans-Hermann Wessels",
"Carlos Fernandez-Granda",
"Yuhan Hao"
] |
Poster
|
Combinatorial CRISPR screening enables large-scale identification of synergistic gene pairs for combination therapies, but exhaustive experimentation is infeasible. We introduce NAIAD, an active learning framework that efficiently discovers optimal gene pairs by leveraging single-gene perturbation effects and adaptive gene embeddings that scale with the training data size, mitigating overfitting in small-sample learning while capturing complex gene interactions as more data is collected. Evaluated on four CRISPR datasets with over 350,000 interactions, NAIAD trained on small datasets outperforms existing models by up to 40\%. Its recommendation system prioritizes gene pairs with maximum predicted effects, accelerating discovery with fewer experiments. We also extend NAIAD to optimal drug combination identification among 2,000 candidates. Overall, NAIAD enhances combinatorial perturbation design and drives advances in genomics research and therapeutic development in combination therapy. Our code is publicly available at: https://github.com/NeptuneBio/NAIAD
|
active learning, AI and Lab Loop, combination therapy, combinatorial perturbation
| null | 12,953 |
2411.12010
|
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] |
https://github.com/neptunebio/naiad
|
575 |
Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees
|
https://openreview.net/forum?id=OrkMLgWoiG
|
[
"Tomer Meir",
"Uri Shalit",
"Malka Gorfine"
] |
Poster
|
Tailoring treatments to individual needs is a central goal in fields such as medicine. A key step toward this goal is estimating Heterogeneous Treatment Effects (HTE)—the way treatments impact different subgroups. While crucial, HTE estimation is challenging with survival data, where time until an event (e.g., death) is key. Existing methods often assume complete observation, an assumption violated in survival data due to right-censoring, leading to bias and inefficiency. Cui et al. (2023) proposed a doubly-robust method for HTE estimation in survival data under no hidden confounders, combining a causal survival forest with an augmented inverse-censoring weighting estimator. However, we find it struggles under heavy censoring, which is common in rare-outcome problems such as Amyotrophic lateral sclerosis (ALS). Moreover, most current methods cannot handle instrumental variables, which are a crucial tool in the causal inference arsenal. We introduce Multiple Imputation for Survival Treatment Response (MISTR), a novel, general, and non-parametric method for estimating HTE in survival data. MISTR uses recursively imputed survival trees to handle censoring without directly modeling the censoring mechanism. Through extensive simulations and analysis of two real-world datasets—the AIDS Clinical Trials Group Protocol 175 and the Illinois unemployment dataset we show that MISTR outperforms prior methods under heavy censoring in the no-hidden-confounders setting, and extends to the instrumental variable setting. To our knowledge, MISTR is the first non-parametric approach for HTE estimation with unobserved confounders via instrumental variables.
|
Heterogeneous treatment effect, Causal inference, Time to event data, Survival analysis, Instrumental variable, Rare disease, Multiple imputations
|
A non-parametric method for estimating heterogeneous treatment effects in survival data, overcoming prior limitations in heavy censoring and confounding. We demonstrate its superiority in both no-hidden-confounders and instrumental variable settings.
| 12,947 |
2502.01575
|
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] |
https://github.com/tomer1812/mistr
|
576 |
On Explaining Equivariant Graph Networks via Improved Relevance Propagation
|
https://openreview.net/forum?id=G2mAZTpJcz
|
[
"Hongyi Ling",
"Haiyang Yu",
"Zhimeng Jiang",
"Na Zou",
"Shuiwang Ji"
] |
Poster
|
We consider explainability in equivariant graph neural networks for 3D geometric graphs. While many XAI methods have been developed for analyzing graph neural networks, they predominantly target 2D graph structures. The complex nature of 3D data and the sophisticated architectures of equivariant GNNs present unique challenges. Current XAI techniques either struggle to adapt to equivariant GNNs or fail to effectively handle positional data and evaluate the significance of geometric features adequately.
To address these challenges, we introduce a novel method, known as EquiGX, which uses the Deep Taylor decomposition framework to extend the layer-wise relevance propagation rules tailored for spherical equivariant GNNs. Our approach decomposes prediction scores and back-propagates the relevance scores through each layer to the input space. Our decomposition rules provide a detailed explanation of each layer’s contribution to the network’s predictions, thereby enhancing our understanding of how geometric and positional data influence the model’s outputs.
Through experiments on both synthetic and real-world datasets, our method demonstrates its capability to identify critical geometric structures and outperform alternative baselines. These results indicate that our method provides significantly enhanced explanations for equivariant GNNs. Our code has been released as part of the AIRS library (https://github.com/divelab/AIRS/).
|
equivariant GNNs, XAI
| null | 12,944 | null |
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] | null |
577 |
Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge
|
https://openreview.net/forum?id=PNRznmmWP7
|
[
"Swarnadeep Saha",
"Xian Li",
"Marjan Ghazvininejad",
"Jason E Weston",
"Tianlu Wang"
] |
Poster
|
LLM-as-a-Judge models generate chain-of-thought (CoT) sequences intended to capture the step-by-step reasoning process that underlies the final evaluation of a response. However, due to the lack of human-annotated CoTs for evaluation, the required components and structure of effective reasoning traces remain understudied. Consequently, previous approaches often (1) constrain reasoning traces to hand-designed components, such as a list of criteria, reference answers, or verification questions and (2) structure them such that planning is intertwined with the reasoning for evaluation. In this work, we propose EvalPlanner, a preference optimization algorithm for Thinking-LLM-as-a-Judge that first generates an unconstrained evaluation plan, followed by its execution, and then the final judgment. In a self-training loop, EvalPlanner iteratively optimizes over synthetically constructed evaluation plans and executions, leading to better final verdicts. Our method achieves a new state-of-the-art performance for generative reward models on RewardBench and PPE, despite being trained on fewer amount of, and synthetically generated, preference pairs. Additional experiments on other benchmarks like RM-Bench, JudgeBench, and FollowBenchEval further highlight the utility of both planning and reasoning for building robust LLM-as-a-Judge reasoning models.
|
LLM-as-a-Judge, Planning, Reasoning, Evaluation
| null | 12,919 | null |
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] | null |
578 |
Adaptive Sample Sharing for Multi Agent Linear Bandits
|
https://openreview.net/forum?id=qQBzVFwtPN
|
[
"Hamza Cherkaoui",
"Merwan Barlier",
"Igor Colin"
] |
Poster
|
The multi-agent linear bandit setting is a well-known setting for which designing efficient collaboration between agents remains challenging. This paper studies the impact of data sharing among agents on regret minimization. Unlike most existing approaches, our contribution does not rely on any assumptions on the bandit parameters structure. Our main result formalizes the trade-off between the bias and uncertainty of the bandit parameter estimation for efficient collaboration. This result is the cornerstone of the Bandit Adaptive Sample Sharing (BASS) algorithm, whose efficiency over the current state-of-the-art is validated through both theoretical analysis and empirical evaluations on both synthetic and real-world datasets. Furthermore, we demonstrate that, when agents' parameters display a cluster structure, our algorithm accurately recovers them.
|
linear bandit, collaboration, sample sharing
|
This paper addresses the agent collaboration in linear bandit problems via sample sharing, deriving an effective bias-variance trade-off for regret minimization.
| 12,916 |
2309.08710
|
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] |
https://github.com/hcherkaoui/collaborative_bandits
|
579 |
Geometric Contact Flows: Contactomorphisms for Dynamics and Control
|
https://openreview.net/forum?id=v2nQ1e78Rc
|
[
"Andrea Testa",
"Søren Hauberg",
"Tamim Asfour",
"Leonel Rozo"
] |
Poster
|
Accurately modeling and predicting complex dynamical systems, particularly those involving force exchange and dissipation, is crucial for applications ranging from fluid dynamics to robotics, but presents significant challenges due to the intricate interplay of geometric constraints and energy transfer. This paper introduces Geometric Contact Flows (GFC), a novel framework leveraging Riemannian and Contact geometry as inductive biases to learn such systems. GCF constructs a latent contact Hamiltonian model encoding desirable properties like stability or energy conservation. An ensemble of contactomorphisms then adapts this model to the target dynamics while preserving these properties. This ensemble allows for uncertainty-aware geodesics that attract the system’s behavior toward the data support, enabling robust generalization and adaptation to unseen scenarios. Experiments on learning dynamics for physical systems and for controlling robots on interaction tasks demonstrate the effectiveness of our approach.
|
Contact geometry, Riemannian geometry, contact Hamiltonian dynamics, Dynamical systems, Diffeomorphisms
|
Leveraging Riemannian and Contact geometry as inductive biases to learn and control complex dynamical systems.
| 12,913 |
2506.17868
|
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] | null |
580 |
Regression for the Mean: Auto-Evaluation and Inference with Few Labels through Post-hoc Regression
|
https://openreview.net/forum?id=kRKsUOJdp5
|
[
"Benjamin Eyre",
"David Madras"
] |
Poster
|
The availability of machine learning systems that can effectively perform arbitrary tasks has led to synthetic labels from these systems being used in applications of statistical inference, such as data analysis or model evaluation. The Prediction Powered Inference (PPI) framework provides a way of leveraging both a large pool of pseudo-labelled data and a small sample with real, high-quality labels to produce a low-variance, unbiased estimate of the quantity being evaluated for. Most work on PPI considers a relatively sizable set of labelled samples, which can be resource intensive to obtain. However, we find that when labelled data is scarce, the PPI++ method can perform even worse than classical inference. We analyze this phenomenon by relating PPI++ to ordinary least squares regression, which also experiences high variance with small sample sizes, and use this regression framework to better understand the efficacy of PPI. Motivated by this, we present two new PPI-based techniques that leverage robust regressors to produce even lower variance estimators in the few-label regime
|
auto-evaluation, prediction-powered-inference, regression, variance-reduction
| null | 12,908 |
2411.12665
|
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] | null |
581 |
Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes
|
https://openreview.net/forum?id=SUX6Wzy9t1
|
[
"Sidhanth Holalkere",
"David Bindel",
"Silvia Sellán",
"Alexander Terenin"
] |
Poster
|
Poisson Surface Reconstruction is a widely-used algorithm for reconstructing a surface from an oriented point cloud. To facilitate applications where only partial surface information is available, or scanning is performed sequentially, a recent line of work proposes to incorporate uncertainty into the reconstructed surface via Gaussian process models. The resulting algorithms first perform Gaussian process interpolation, then solve a set of volumetric partial differential equations globally in space, resulting in a computationally expensive two-stage procedure. In this work, we apply recently-developed techniques from geometric Gaussian processes to combine interpolation and surface reconstruction into a single stage, requiring only one linear solve per sample. The resulting reconstructed surface samples can be queried locally in space, without the use of problem-dependent volumetric meshes or grids. These capabilities enable one to (a) perform probabilistic collision detection locally around the region of interest, (b) perform ray casting without evaluating points not on the ray's trajectory, and (c) perform next-view planning on a per-ray basis. They also do not requiring one to approximate kernel matrix inverses with diagonal matrices as part of intermediate computations, unlike prior methods. Results show that our approach provides a cleaner, more-principled, and more-flexible stochastic surface reconstruction pipeline.
|
stochastic poisson surface reconstruction, geometric gaussian processes, computer graphics
| null | 12,905 |
2503.19136
|
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] |
https://github.com/sholalkere/GeoSPSR
|
582 |
Communicating Activations Between Language Model Agents
|
https://openreview.net/forum?id=W6RPXUUFic
|
[
"Vignav Ramesh",
"Kenneth Li"
] |
Poster
|
Communication between multiple language model (LM) agents has been shown to scale up the reasoning ability of LMs. While natural language has been the dominant medium for inter-LM communication, it is not obvious this should be the standard: not only does natural language communication incur high inference costs that scale quickly with the number of both agents and messages, but also the decoding process abstracts away too much rich information that could be otherwise accessed from the internal activations. In this work, we propose a simple technique whereby LMs communicate via *activations*; concretely, we pause an LM $B$'s computation at an intermediate layer, combine its current activation with another LM $A$'s intermediate activation via some function $f$, then pass $f$'s output into the next layer of $B$ and continue the forward pass till decoding is complete. This approach scales up LMs on new tasks with *zero* additional parameters and data, and saves a *substantial amount of compute* over natural language communication. We test our method with various functional forms $f$ on two experimental setups—multi-player coordination games and reasoning benchmarks—and find that it achieves up to $27$% improvement over natural language communication across datasets with $<$$1/4$ the compute, illustrating the superiority and robustness of activations as an alternative "language" for communication between LMs.
|
large language models, multiagent communication, embedding representation, multiagent debate
|
We propose a simple technique whereby language models communicate via activations, which outperforms traditional natural language communication at a fraction of the compute required.
| 12,901 |
2501.14082
|
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] | null |
583 |
Extracting Rare Dependence Patterns via Adaptive Sample Reweighting
|
https://openreview.net/forum?id=iIPAdNq9cq
|
[
"Yiqing Li",
"Yewei Xia",
"Xiaofei Wang",
"Zhengming Chen",
"Liuhua Peng",
"Mingming Gong",
"Kun Zhang"
] |
Poster
|
Discovering dependence patterns between variables from observational data is a fundamental issue in data analysis. However, existing testing methods often fail to detect subtle yet critical patterns that occur within small regions of the data distribution--patterns we term rare dependence. These rare dependencies obscure the true underlying dependence structure in variables, particularly in causal discovery tasks.
To address this issue, we propose a novel testing method that combines kernel-based (conditional) independence testing with adaptive sample importance reweighting. By learning and assigning higher importance weights to data points exhibiting significant dependence, our method amplifies the patterns and can detect them successfully. Theoretically, we analyze the asymptotic distributions of the statistics in this method and show the uniform bound of the learning scheme. Furthermore, we integrate our tests into the PC algorithm, a constraint-based approach for causal discovery, equipping it to uncover causal relationships even in the presence of rare dependence. Empirical evaluation of synthetic and real-world datasets comprehensively demonstrates the efficacy of our method.
|
Independence tests, Sample reweighting, Causal discovery
| null | 12,900 | null |
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] |
https://github.com/leeedwina430/RKCIT
|
584 |
Online Learning in Risk Sensitive constrained MDP
|
https://openreview.net/forum?id=5s7D7FPuTc
|
[
"Arnob Ghosh",
"Mehrdad Moharrami"
] |
Poster
|
We consider a setting in which the agent aims to maximize the expected cumulative reward, subject to a constraint that the entropic risk of the total utility exceeds a given threshold. Unlike the risk-neutral case, standard primal-dual approaches fail to directly yield regret and violation bounds, as value iteration with respect to a combined state-action value function is not applicable in the risk-sensitive setting. To address this, we adopt the Optimized Certainty Equivalent (OCE) representation of the entropic risk measure and reformulate the problem by augmenting the state space with a continuous budget variable. We then propose a primal-dual algorithm tailored to this augmented formulation. In contrast to the standard approach for risk-neutral CMDPs, our method incorporates a truncated dual update to account for the possible absence of strong duality. We show that the proposed algorithm achieves regret of $\tilde{\mathcal{O}}\big(V_{g,\max}K^{3/4} + \sqrt{H^4 S^2 A \log(1/\delta)}K^{3/4}\big)$ and constraint violation of $\tilde{\mathcal{O}}\big(V_{g,\max} \sqrt{ {H^3 S^2 A \log(1/\delta)}}K^{3/4} \big)$ with probability at least $1-\delta$, where $S$ and $A$ denote the cardinalities of the state and action spaces, respectively, $H$ is the episode length, $K$ is the number of episodes, $\alpha < 0$ is the risk-aversion parameter, and $V_{g,\max} = \frac{1}{|\alpha|}(\exp(|\alpha|H) - 1)$. *To the best of our knowledge, this is the first result establishing sublinear regret and violation bounds for the risk-sensitive CMDP problem.*
|
Augmented State, Constrained MDP, Risk sensitive RL, Robust RL, Primal-Dual algorithm
|
This is the first result that shows sub-linear regret and violation result for risk sensitive constrained MDP
| 12,898 | null |
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] |
https://github.com/mmoharami/Risk-Sensitive-CMDP
|
585 |
Self-Play Q-Learners Can Provably Collude in the Iterated Prisoner's Dilemma
|
https://openreview.net/forum?id=q6zwUeWkZL
|
[
"Quentin Bertrand",
"Juan Agustin Duque",
"Emilio Calvano",
"Gauthier Gidel"
] |
Poster
|
A growing body of computational studies shows that simple machine learning agents converge to cooperative behaviors in social dilemmas, such as collusive price-setting in oligopoly markets, raising questions about what drives this outcome. In this work, we provide theoretical foundations for this phenomenon in the context of self-play multi-agent Q-learners in the iterated prisoner’s dilemma. We characterize broad conditions under which such agents provably learn the cooperative Pavlov (win-stay, lose-shift) policy rather than the Pareto-dominated “always defect” policy. We validate our theoretical results through additional experiments, demonstrating their robustness across a broader class of deep learning algorithms.
|
iterated prisoner's dilemma, cooperation, Q-learning, self-play
|
We show that self-play Q-learners can learn to collude, even if their inital states and policies are always defect
| 12,897 | null |
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] | null |
586 |
Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification
|
https://openreview.net/forum?id=vfqdJy12Kk
|
[
"Flavio Petruzzellis",
"Cristina Cornelio",
"Pietro Lio"
] |
Poster
|
Large Language Models (LLMs) have shown promise as robotic planners but often struggle with long-horizon and complex tasks, especially in specialized environments requiring external knowledge. While hierarchical planning and Retrieval-Augmented Generation (RAG) address some of these challenges, they remain insufficient on their own and a deeper integration is required for achieving more reliable systems. To this end, we propose a neuro-symbolic approach that enhances LLMs-based planners with Knowledge Graph-based RAG for hierarchical plan generation. This method decomposes complex tasks into manageable subtasks, further expanded into executable atomic action sequences. To ensure formal correctness and proper decomposition, we integrate a Symbolic Validator, which also functions as a failure detector by aligning expected and observed world states. Our evaluation against baseline methods demonstrates the consistent significant advantages of integrating hierarchical planning, symbolic verification, and RAG across tasks of varying complexity and different LLMs. Additionally, our experimental setup and novel metrics not only validate our approach for complex planning but also serve as a tool for assessing LLMs' reasoning and compositional capabilities. Code available at https://github.com/corneliocristina/HVR.
|
Neuro-Symbolic, Hierarchical Planning, Robotics, Symbolic Verification, Knowledge Graph RAG
|
We propose a neurosymbolic method combining hierarchical planning, KG-RAG, and symbolic validation to enhance LLMs for complex task planning, ensuring correctness, failure detection, and offering a tool to evaluate LLM reasoning/compositional skills.
| 12,895 |
2504.04578
|
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0.031290024518966675,
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] |
https://github.com/corneliocristina/HVR
|
587 |
Diffusion on Language Model Encodings for Protein Sequence Generation
|
https://openreview.net/forum?id=xB9eROwBCB
|
[
"Viacheslav Meshchaninov",
"Pavel Strashnov",
"Andrey Shevtsov",
"Fedor Nikolaev",
"Nikita Ivanisenko",
"Olga Kardymon",
"Dmitry Vetrov"
] |
Poster
|
Protein *sequence* design has seen significant advances through discrete diffusion and autoregressive approaches, yet the potential of continuous diffusion remains underexplored. Here, we present *DiMA*, a latent diffusion framework that operates on protein language model representations. Through systematic exploration of architectural choices and diffusion components, we develop a robust methodology that generalizes across multiple protein encoders ranging from 8M to 3B parameters. We demonstrate that our framework achieves consistently high performance across sequence-only (ESM-2, ESMc), dual-decodable (CHEAP), and multimodal (SaProt) representations using the same architecture and training approach. We conduct extensive evaluation of existing methods alongside *DiMA* using multiple metrics across two protein modalities, covering quality, diversity, novelty, and distribution matching of generated proteins. *DiMA* consistently produces novel, high-quality and diverse protein sequences and achieves strong results compared to baselines such as autoregressive, discrete diffusion and flow matching language models. The model demonstrates versatile functionality, supporting conditional generation tasks including protein family-generation, motif scaffolding and infilling, and fold-specific sequence design, despite being trained solely on sequence data. This work provides a universal continuous diffusion framework for protein sequence generation, offering both architectural insights and practical applicability across various protein design scenarios. Code is released at [GitHub](https://github.com/MeshchaninovViacheslav/DiMA).
|
continuous diffusion, generative protein design, score matching, denoising models, sequence modeling
|
A continuous latent diffusion framework for protein sequence generation with strong performance and versatile conditional generation capabilities.
| 12,885 |
2403.03726
|
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] |
https://github.com/MeshchaninovViacheslav/DiMA
|
588 |
Stacey: Promoting Stochastic Steepest Descent via Accelerated ℓp-Smooth Nonconvex Optimization
|
https://openreview.net/forum?id=TaqwI9qF5Q
|
[
"Xinyu Luo",
"Site Bai",
"Bolian Li",
"Petros Drineas",
"Ruqi Zhang",
"Brian Bullins"
] |
Poster
|
While popular optimization methods such as SGD, AdamW, and Lion depend on steepest descent updates in either $\ell_2$ or $\ell_\infty$ norms, there remains a critical gap in handling the non-Euclidean structure observed in modern deep networks training. In this work, we address this need by introducing a new accelerated $\ell_p$ steepest descent algorithm, called Stacey, which uses interpolated primal-dual iterate sequences to effectively navigate non-Euclidean smooth optimization tasks. In addition to providing novel theoretical guarantees for the foundations of our algorithm, we empirically compare our approach against these popular methods on tasks including image classification and language model (LLM) pretraining, demonstrating both faster convergence and higher final accuracy. We further evaluate different values of $p$ across various models and datasets, underscoring the importance and efficiency of non-Euclidean approaches over standard Euclidean methods. Code can be found at https://github.com/xinyuluo8561/Stacey.
|
Non-convex Optimization, Non-Euclidean Acceleration, Stochastic Steepest Descent
|
We propose Stacey, an accelerated $\ell_p$ steepest descent algorithm that leverages non-Euclidean geometry to achieve faster convergence and higher accuracy.
| 12,883 | null |
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] | null |
589 |
Verification Learning: Make Unsupervised Neuro-Symbolic System Feasible
|
https://openreview.net/forum?id=aAkq9mMviY
|
[
"Lin-Han Jia",
"Wen-Chao Hu",
"Jie-Jing Shao",
"Lan-Zhe Guo",
"Yu-Feng Li"
] |
Poster
|
The current Neuro-Symbolic (NeSy) Learning paradigm suffers from an over-reliance on labeled data, so if we completely disregard labels, it leads to less symbol information, a larger solution space, and more shortcuts—issues that current Nesy systems cannot resolve. This paper introduces a novel learning paradigm, Verification Learning (VL), which addresses this challenge by transforming the label-based reasoning process in Nesy into a label-free verification process. VL achieves excellent learning results solely by relying on unlabeled data and a function that verifies whether the current predictions conform to the rules. We formalize this problem as a Constraint Optimization Problem (COP) and propose a Dynamic Combinatorial Sorting (DCS) algorithm that accelerates the solution by reducing verification attempts, effectively lowering computational costs and introduce a prior alignment method to address potential shortcuts. Our theoretical analysis points out which tasks in Nesy systems can be completed without labels and explains why rules can replace infinite labels for some tasks, while for others the rules have no effect. We validate the proposed framework through several fully unsupervised tasks including addition, sort, match, and chess, each showing significant performance and efficiency improvements.
|
Neural Symbolic Learning
| null | 12,876 |
2503.12917
|
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] | null |
590 |
Reinforce LLM Reasoning through Multi-Agent Reflection
|
https://openreview.net/forum?id=6k3oFS3Lbl
|
[
"Yurun Yuan",
"Tengyang Xie"
] |
Poster
|
Leveraging more test-time computation has proven to be an effective way to boost the reasoning capabilities of large language models (LLMs). Among various methods, the verify-and-improve paradigm stands out for enabling dynamic solution exploration and feedback incorporation. However, existing approaches often suffer from restricted feedback spaces and lack of coordinated training of different parties, leading to suboptimal performance. To address this, we model this multi-turn refinement process as a Markov Decision Process and introduce DPSDP (**D**irect **P**olicy **S**earch by **D**ynamic **P**rogramming), a reinforcement learning algorithm that trains an actor-critic LLM system to iteratively refine answers via direct preference learning on self-generated data. Theoretically, DPSDP can match the performance of any policy within the training distribution. Empirically, we instantiate DPSDP with various base models and show improvements on both in- and out-of-distribution benchmarks. For example, on benchmark MATH 500, majority voting over five refinement steps increases first-turn accuracy from 58.2% to 63.2% with Ministral-based models. An ablation study further confirms the benefits of multi-agent collaboration and out-of-distribution generalization.
|
Post-training, LLM-based multi-agents, Reinforcement learning, Mathematical reasoning
|
This paper introduces DPSDP, a reinforcement learning algorithm that trains an actor-critic LLM system to iteratively refine answers via direct preference learning on self-generated data.
| 12,870 |
2506.08379
|
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] | null |
591 |
QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions
|
https://openreview.net/forum?id=JwZVPTTEwO
|
[
"Zhun Deng",
"Thomas P Zollo",
"Benjamin Eyre",
"Amogh Inamdar",
"David Madras",
"Richard Zemel"
] |
Poster
|
As machine learning models grow increasingly competent, their predictions can supplement scarce or expensive data in various important domains. In support of this paradigm, algorithms have emerged to combine a small amount of high-fidelity observed data with a much larger set of imputed model outputs to estimate some quantity of interest. Yet current hybrid-inference tools target only means or single quantiles, limiting their applicability for many critical domains and use cases. We present QuEst, a principled framework to merge observed and imputed data to deliver point estimates and rigorous confidence intervals for a wide family of quantile-based distributional measures. QuEst covers a range of measures, from tail risk (CVaR) to population segments such as quartiles, that are central to fields such as economics, sociology, education, medicine, and more. We extend QuEst to multidimensional metrics, and introduce an additional optimization technique to further reduce variance in this and other hybrid estimators. We demonstrate the utility of our framework through experiments in economic modeling, opinion polling, and language model auto-evaluation.
|
prediction-powered inference, L-statistics, auto-evaluation, distortion risk measures, synthetic data
|
Rigorous method for combining real, observed data with machine learning model predictions to produce better estimates of important distributional risk measures.
| 12,866 |
2507.05220
|
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] | null |
592 |
Generalization Analysis for Controllable Learning
|
https://openreview.net/forum?id=9UN0E5DO5M
|
[
"Yi-Fan Zhang",
"Xiao Zhang",
"Min-Ling Zhang"
] |
Poster
|
Controllability has become a critical issue in trustworthy machine learning, as a controllable learner allows for dynamic model adaptation to task requirements during testing. However, existing research lacks a comprehensive understanding of how to effectively measure and analyze the generalization performance of controllable learning methods. In an attempt to move towards this goal from a generalization perspective, we first establish a unified framework for controllable learning. Then, we develop a novel vector-contraction inequality and derive a tight generalization bound for general controllable learning classes, which is independent of the number of task targets except for logarithmic factors and represents the current best-in-class theoretical result. Furthermore, we derive generalization bounds for two typical controllable learning methods: embedding-based and hypernetwork-based methods. We also upper bound the Rademacher complexities of commonly used control and prediction functions, which serve as modular theoretical components for deriving generalization bounds for specific controllable learning methods in practical applications such as recommender systems. Our theoretical results without strong assumptions provide general theoretical guarantees for controllable learning methods and offer new insights into understanding controllability in machine learning.
|
Controllability, Generalization Analysis
| null | 12,865 | null |
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] | null |
593 |
Simple Path Structural Encoding for Graph Transformers
|
https://openreview.net/forum?id=t3zwUqibMq
|
[
"Louis Airale",
"Antonio Longa",
"Mattia Rigon",
"Andrea Passerini",
"Roberto Passerone"
] |
Poster
|
Graph transformers extend global self-attention to graph-structured data, achieving notable success in graph learning. Recently, random walk structural encoding (RWSE) has been found to further enhance their predictive power by encoding both structural and positional information into the edge representation. However, RWSE cannot always distinguish between edges that belong to different local graph patterns, which reduces its ability to capture the full structural complexity of graphs.
This work introduces Simple Path Structural Encoding (SPSE), a novel method that utilizes simple path counts for edge encoding. We show theoretically and experimentally that SPSE overcomes the limitations of RWSE, providing a richer representation of graph structures, particularly in capturing local cyclic patterns. To make SPSE computationally tractable, we propose an efficient approximate algorithm for simple path counting.
SPSE demonstrates significant performance improvements over RWSE on various benchmarks, including molecular and long-range graph datasets, achieving statistically significant gains in discriminative tasks. These results pose SPSE as a powerful edge encoding alternative for enhancing the expressivity of graph transformers.
|
Graph transformer, structural encoding, simple paths
|
We present a structural encoding based on simple path counts for graph transformer, with theoretical and experimental validation.
| 12,864 |
2502.09365
|
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] |
https://github.com/LouisBearing/Graph-SPSE-Encoding
|
594 |
Towards characterizing the value of edge embeddings in Graph Neural Networks
|
https://openreview.net/forum?id=AEHtq4xHSU
|
[
"Dhruv Rohatgi",
"Tanya Marwah",
"Zachary Chase Lipton",
"Jianfeng Lu",
"Ankur Moitra",
"Andrej Risteski"
] |
Poster
|
Graph neural networks (GNNs) are the dominant approach to solving machine learning problems defined over graphs. Despite much theoretical and empirical work in recent years, our understanding of finer-grained aspects of architectural design for GNNs remains impoverished. In this paper, we consider the benefits of architectures that maintain and update edge embeddings. On the theoretical front, under a suitable computational abstraction for a layer in the model, as well as memory constraints on the embeddings, we show that there are natural tasks on graphical models for which architectures leveraging edge embeddings can be much shallower. Our techniques are inspired by results on time-space tradeoffs in theoretical computer science. Empirically, we show architectures that maintain edge embeddings almost always improve on their node-based counterparts---frequently significantly so in topologies that have "hub" nodes.
|
graph neural networks, theory, representational power, communication complexity, memory tradeoffs, edge embeddings
|
We characterize the added representational power conferred to by edge embeddings in GNNs, considering additional constraints like memory and symmetry.
| 12,858 |
2410.09867
|
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] | null |
595 |
Designing Cyclic Peptides via Harmonic SDE with Atom-Bond Modeling
|
https://openreview.net/forum?id=ERu2ZiAnR7
|
[
"Xiangxin Zhou",
"Mingyu Li",
"Yi Xiao",
"Jiahan Li",
"Dongyu Xue",
"Zaixiang Zheng",
"Jianzhu Ma",
"Quanquan Gu"
] |
Poster
|
Cyclic peptides offer inherent advantages in pharmaceuticals. For example, cyclic peptides are more resistant to enzymatic hydrolysis compared to linear peptides and usually exhibit excellent stability and affinity. Although deep generative models have achieved great success in linear peptide design, several challenges prevent the development of computational methods for designing diverse types of cyclic peptides. These challenges include the scarcity of 3D structural data on target proteins and associated cyclic peptide ligands, the geometric constraints that cyclization imposes, and the involvement of non-canonical amino acids in cyclization. To address the above challenges, we introduce CpSDE, which consists of two key components: AtomSDE, a generative structure prediction model based on harmonic SDE, and ResRouter, a residue type predictor. Utilizing a routed sampling algorithm that alternates between these two models to iteratively update sequences and structures, CpSDE facilitates the generation of cyclic peptides. By employing explicit all-atom and bond modeling, CpSDE overcomes existing data limitations and is proficient in designing a wide variety of cyclic peptides.
Our experimental results demonstrate that the cyclic peptides designed by our method exhibit reliable stability and affinity.
|
cyclic peptide, peptide design, SDE-based generative model
| null | 12,856 |
2505.21452
|
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] | null |
596 |
Sparse Autoencoders for Hypothesis Generation
|
https://openreview.net/forum?id=4R0pugRyN5
|
[
"Rajiv Movva",
"Kenny Peng",
"Nikhil Garg",
"Jon Kleinberg",
"Emma Pierson"
] |
Poster
|
We describe HypotheSAEs, a general method to hypothesize interpretable relationships between text data (e.g., headlines) and a target variable (e.g., clicks). HypotheSAEs has three steps: (1) train a sparse autoencoder on text embeddings to produce interpretable features describing the data distribution, (2) select features that predict the target variable, and (3) generate a natural language interpretation of each feature (e.g., *mentions being surprised or shocked*) using an LLM. Each interpretation serves as a hypothesis about what predicts the target variable. Compared to baselines, our method better identifies reference hypotheses on synthetic datasets (at least +0.06 in F1) and produces more predictive hypotheses on real datasets (~twice as many significant findings), despite requiring 1-2 orders of magnitude less compute than recent LLM-based methods. HypotheSAEs also produces novel discoveries on two well-studied tasks: explaining partisan differences in Congressional speeches and identifying drivers of engagement with online headlines.
|
interpretability, hypothesis generation, sparse autoencoders, computational social science, topic modeling
|
We propose HypotheSAEs, a sparse autoencoder-based method to hypothesize interpretable relationships between input texts and a target variable; we show our method performs well on several computational social science datasets.
| 12,839 |
2502.04382
|
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] |
https://github.com/rmovva/HypotheSAEs
|
597 |
Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks
|
https://openreview.net/forum?id=ruSU7xtH6v
|
[
"Yuhang Cai",
"Kangjie Zhou",
"Jingfeng Wu",
"Song Mei",
"Michael Lindsey",
"Peter Bartlett"
] |
Poster
|
We establish the asymptotic implicit bias of gradient descent (GD) for generic non-homogeneous deep networks under exponential loss. Specifically, we characterize three key properties of GD iterates starting from a sufficiently small empirical risk, where the threshold is determined by a measure of the network's non-homogeneity. First, we show that a normalized margin induced by the GD iterates increases nearly monotonically. Second, we prove that while the norm of the GD iterates diverges to infinity, the iterates themselves converge in direction. Finally, we establish that this directional limit satisfies the Karush–Kuhn–Tucker (KKT) conditions of a margin maximization problem. Prior works on implicit bias have focused exclusively on homogeneous networks; in contrast, our results apply to a broad class of non-homogeneous networks satisfying a mild near-homogeneity condition. In particular, our results apply to networks with residual connections and non-homogeneous activation functions, thereby resolving an open problem posed byJi & Telgarsky (2020).
|
Implicit bias, Non-homogeneous model, Deep neural networks, Gradient descent
| null | 12,836 |
2502.16075
|
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] | null |
598 |
To Each Metric Its Decoding: Post-Hoc Optimal Decision Rules of Probabilistic Hierarchical Classifiers
|
https://openreview.net/forum?id=5zsBvPOIUQ
|
[
"Roman Plaud",
"Alexandre Perez-Lebel",
"Matthieu Labeau",
"Antoine Saillenfest",
"Thomas Bonald"
] |
Poster
|
Hierarchical classification offers an approach to incorporate the concept of mistake severity by leveraging a structured, labeled hierarchy. However, decoding in such settings frequently relies on heuristic decision rules, which may not align with task-specific evaluation metrics. In this work, we propose a framework for the optimal decoding of an output probability distribution with respect
to a target metric. We derive optimal decision rules for increasingly complex prediction settings, providing universal algorithms when candidates are limited to the set of nodes. In the most general case of predicting a *subset of nodes*, we focus on rules dedicated to the hierarchical $\mathrm{hF}_{\beta}$ scores, tailored to hierarchical settings. To demonstrate the practical utility of our approach, we conduct
extensive empirical evaluations, showcasing the superiority of our proposed optimal strategies, particularly in underdetermined scenarios. These results highlight the potential of our methods to enhance the performance and reliability of hierarchical classifiers in real-world applications.
|
Hierarchical Classification, Optimal Decoding
|
We propose an optimal decoding framework for hierarchical classification, deriving decision rules that optimize hierarchical metrics.
| 12,830 |
2506.01552
|
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] |
https://github.com/RomanPlaud/hierarchical_decision_rules
|
599 |
Polynomial-Time Approximability of Constrained Reinforcement Learning
|
https://openreview.net/forum?id=bcHMa96Dv6
|
[
"Jeremy McMahan"
] |
Poster
|
We study the computational complexity of approximating general constrained Markov decision processes. Our primary contribution is the design of a polynomial time $(0,\epsilon)$-additive bicriteria approximation algorithm for finding optimal constrained policies across a broad class of recursively computable constraints, including almost-sure, chance, expectation, and their anytime variants. Matching lower bounds imply our approximation guarantees are optimal so long as $P \neq NP$. The generality of our approach results in answers to several long-standing open complexity questions in the constrained reinforcement learning literature. Specifically, we are the first to prove polynomial-time approximability for the following settings: policies under chance constraints, deterministic policies under multiple expectation constraints, policies under non-homogeneous constraints (i.e., constraints of different types), and policies under constraints for continuous-state processes.
|
Constrained Reinforcement Learning, Computational Complexity, Approximation Algorithms
| null | 12,829 |
2502.07764
|
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] | null |
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