Most Cited ICML "ode-based neural networks" Papers

Chip Placement with Diffusion Models

Vint Lee, Minh Nguyen, Leena Elzeiny et al.

ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models

Dongha Kim, Jaesung Hwang, Jongjin Lee et al.

Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL

Qin-Wen Luo, Ming-Kun Xie, Ye-Wen Wang et al.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation

Bowen Zheng, Tianming Yang

RVI-SAC: Average Reward Off-Policy Deep Reinforcement Learning

Yukinari Hisaki, Isao Ono

Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction

Lars van der Laan, Ahmed Alaa

Environment Design for Inverse Reinforcement Learning

Thomas Kleine Buening, Victor Villin, Christos Dimitrakakis

Beyond Low-rank Decomposition: A Shortcut Approach for Efficient On-Device Learning

Le-Trung Nguyen, Aël Quélennec, Van-Tam Nguyen et al.

Product of Experts with LLMs: Boosting Performance on ARC Is a Matter of Perspective

Daniel Franzen, Jan Disselhoff, David Hartmann

Neural Solver Selection for Combinatorial Optimization

Chengrui Gao, Haopu Shang, Ke Xue et al.

On the Weight Dynamics of Deep Normalized Networks

Christian H.X. Ali Mehmeti-Göpel, Michael Wand

Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks

Mikkel Jordahn, Pablo Olmos

SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models

Cansu Sancaktar, Christian Gumbsch, Andrii Zadaianchuk et al.

Semantic Shift Estimation via Dual-Projection and Classifier Reconstruction for Exemplar-Free Class-Incremental Learning

Run He, Di Fang, Yicheng Xu et al.

Graph As Point Set

Xiyuan Wang, Pan Li, Muhan Zhang

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations

Haotian Si, Changhua Pei, Jianhui LI et al.

Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning

Laixi Shi, Jingchu Gai, Eric Mazumdar et al.

Reward Modeling with Ordinal Feedback: Wisdom of the Crowd

Shang Liu, Yu Pan, Guanting Chen et al.

Efficient Logit-based Knowledge Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deployment

Chengting Yu, Xiaochen Zhao, Lei Liu et al.

Commute Graph Neural Networks

Wei Zhuo, Han Yu, Guang Tan et al.

Projection-Free Variance Reduction Methods for Stochastic Constrained Multi-Level Compositional Optimization

Wei Jiang, Sifan Yang, Wenhao Yang et al.

Online Variational Sequential Monte Carlo

Alessandro Mastrototaro, Jimmy Olsson

Implicit degree bias in the link prediction task

Rachith Aiyappa, Xin Wang, Munjung Kim et al.

Simulation-Based Inference with Quantile Regression

He Jia

Gap-Dependent Bounds for Federated $Q$-Learning

Haochen Zhang, Zhong Zheng, Lingzhou Xue

Non-stationary Diffusion For Probabilistic Time Series Forecasting

Weiwei Ye, Zhuopeng Xu, Ning Gui

LAGMA: LAtent Goal-guided Multi-Agent Reinforcement Learning

Hyungho Na, IL CHUL MOON

Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and Retraining

Aaron Li, Robin Netzorg, Zhihan Cheng et al.

Efficiently Access Diffusion Fisher: Within the Outer Product Span Space

Fangyikang Wang, Hubery Yin, Shaobin Zhuang et al.

AssistanceZero: Scalably Solving Assistance Games

Cassidy Laidlaw, Eli Bronstein, Timothy Guo et al.

PASOA- PArticle baSed Bayesian Optimal Adaptive design

Jacopo Iollo, Christophe Heinkelé, Pierre Alliez et al.

Clustering Properties of Self-Supervised Learning

Xi Weng, Jianing An, Xudong Ma et al.

Geometric Hyena Networks for Large-scale Equivariant Learning

Artem Moskalev, Mangal Prakash, Junjie Xu et al.

CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language Models

Junbo Yin, Chao Zha, Wenjia He et al.

Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought

Zhen-Yu Zhang, Siwei Han, Huaxiu Yao et al.

MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners

Fang-Duo Tsai, Shih-Lun Wu, Weijaw Lee et al.

Low-Rank Adapting Models for Sparse Autoencoders

Matthew Chen, Josh Engels, Max Tegmark

Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning

Wanyun Xie, Francesco Tonin, Volkan Cevher

Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits

Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun

Boosting Masked ECG-Text Auto-Encoders as Discriminative Learners

Hung Manh Pham, Aaqib Saeed, Dong Ma

Does Low Rank Adaptation Lead to Lower Robustness against Training-Time Attacks?

Zi Liang, Haibo Hu, Qingqing Ye et al.

Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models

Yao Shu, Wenyang Hu, See-Kiong Ng et al.

Provable Privacy with Non-Private Pre-Processing

Yaxi Hu, Amartya Sanyal, Bernhard Schölkopf

R2-T2: Re-Routing in Test-Time for Multimodal Mixture-of-Experts

Zhongyang Li, Ziyue Li, Tianyi Zhou

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning

Shiwei Li, Xiandi Luo, Haozhao Wang et al.

Determining Layer-wise Sparsity for Large Language Models Through a Theoretical Perspective

Weizhong Huang, Yuxin Zhang, Xiawu Zheng et al.

Inductive Gradient Adjustment for Spectral Bias in Implicit Neural Representations

Kexuan Shi, Hai Chen, Leheng Zhang et al.

Improving Generalization with Flat Hilbert Bayesian Inference

Tuan Truong, Quyen Tran, Ngoc Quan Pham et al.

Neural NeRF Compression

Tuan Pham, Stephan Mandt

Rethinking Time Encoding via Learnable Transformation Functions

Xi Chen, Yateng Tang, Jiarong Xu et al.

A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs

Kihyuk Hong, Ambuj Tewari

Generalization Error of Graph Neural Networks in the Mean-field Regime

Gholamali Aminian, Yixuan He, Gesine Reinert et al.

Provable In-Context Vector Arithmetic via Retrieving Task Concepts

Dake Bu, Wei Huang, Andi Han et al.

On the Role of Label Noise in the Feature Learning Process

Andi Han, Wei Huang, Zhanpeng Zhou et al.

TimeStep Master: Asymmetrical Mixture of Timestep LoRA Experts for Versatile and Efficient Diffusion Models in Vision

Shaobin Zhuang, Yiwei Guo, Yanbo Ding et al.

Lightspeed Geometric Dataset Distance via Sliced Optimal Transport

Khai Nguyen, Hai Nguyen, Tuan Pham et al.

RankSEG: A Consistent Ranking-based Framework for Segmentation

Ben Dai, Chunlin Li

Differentiable Model Scaling using Differentiable Topk

Kai Liu, Ruohui Wang, Jianfei Gao et al.

TraceGrad: a Framework Learning Expressive SO(3)-equivariant Non-linear Representations for Electronic-Structure Hamiltonian Prediction

Shi Yin, Xinyang Pan, fengyan wang et al.

Bayesian Exploration Networks

Mattie Fellows, Brandon Kaplowitz, Christian Schroeder de Witt et al.

Collaborative Heterogeneous Causal Inference Beyond Meta-analysis

Tianyu Guo, Sai Praneeth Karimireddy, Michael Jordan

Gambling-Based Confidence Sequences for Bounded Random Vectors

Jongha (Jon) Ryu, Gregory Wornell

MetaAgent: Automatically Constructing Multi-Agent Systems Based on Finite State Machines

Yaolun Zhang, Xiaogeng Liu, Chaowei Xiao

On Linear Convergence in Smooth Convex-Concave Bilinearly-Coupled Saddle-Point Optimization: Lower Bounds and Optimal Algorithms

Ekaterina Borodich, Alexander Gasnikov, Dmitry Kovalev

Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models

Ulzee An, Moonseong Jeong, Simon Lee et al.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality

Joshua Southern, Yam Eitan, Guy Bar Shalom et al.

On the Second-Order Convergence of Biased Policy Gradient Algorithms

Siqiao Mu, Diego Klabjan

Projection Optimization: A General Framework for Multi-Objective and Multi-Group RLHF

Nuoya Xiong, Aarti Singh

The underlying structures of self-attention: symmetry, directionality, and emergent dynamics in Transformer training

Matteo Saponati, Pascal J. Sager, Pau Vilimelis Aceituno et al.

A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models

Taehong Moon, Moonseok Choi, EungGu Yun et al.

Design Considerations in Offline Preference-based RL

Alekh Agarwal, Christoph Dann, Teodor Vanislavov Marinov

In-Context Linear Regression Demystified: Training Dynamics and Mechanistic Interpretability of Multi-Head Softmax Attention

Jianliang He, Xintian Pan, Siyu Chen et al.

GRAIL: Graph Edit Distance and Node Alignment using LLM-Generated Code

Samidha Verma, Arushi Goyal, Ananya Mathur et al.

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self Supervised Learning Research

Patrik Reizinger, Randall Balestriero, David Klindt et al.

What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities

Wendong Bu, Yang Wu, Qifan Yu et al.

BARNN: A Bayesian Autoregressive and Recurrent Neural Network

Dario Coscia, Max Welling, Nicola Demo et al.

Prune 'n Predict: Optimizing LLM Decision-making with Conformal Prediction

Harit Vishwakarma, Alan Mishler, Thomas Cook et al.

Reinforcement Learning for Quantum Control under Physical Constraints

Jan Ole Ernst, Aniket Chatterjee, Tim Franzmeyer et al.

Distributional Bellman Operators over Mean Embeddings

Li Kevin Wenliang, Gregoire Deletang, Matthew Aitchison et al.

Partially Observable Reinforcement Learning with Memory Traces

Onno Eberhard, Michael Muehlebach, Claire Vernade

Revisiting Non-Acyclic GFlowNets in Discrete Environments

Nikita Morozov, Ian Maksimov, Daniil Tiapkin et al.

Zero-Shot Offline Imitation Learning via Optimal Transport

Thomas Rupf, Marco Bagatella, Nico Gürtler et al.

Continuous Treatment Effects with Surrogate Outcomes

Zhenghao Zeng, David Arbour, Avi Feller et al.

Event-Customized Image Generation

Zhen Wang, Yilei JIANG, Dong Zheng et al.

Efficient Error Certification for Physics-Informed Neural Networks

Francisco Eiras, Adel Bibi, Rudy Bunel et al.

Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the Squared Loss

Abhijeet Mulgund, Chirag Pabbaraju

Hypo3D: Exploring Hypothetical Reasoning in 3D

Ye Mao, Weixun Luo, Junpeng Jing et al.

StealthInk: A Multi-bit and Stealthy Watermark for Large Language Models

Ya Jiang, Chuxiong Wu, Massieh Kordi Boroujeny et al.

PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses

Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni

A Field Guide for Pacing Budget and ROS Constraints

Santiago Balseiro, Kshipra Bhawalkar, Zhe Feng et al.

Log-Sum-Exponential Estimator for Off-Policy Evaluation and Learning

Armin Behnamnia, Gholamali Aminian, Alireza Aghaei et al.

Learning Decision Trees and Forests with Algorithmic Recourse

Kentaro Kanamori, Takuya Takagi, Ken Kobayashi et al.

Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective

Jiawei Huang, Bingcong Li, Christoph Dann et al.

Making Old Things New: A Unified Algorithm for Differentially Private Clustering

Max Dupre la Tour, Monika Henzinger, David Saulpic

Unveiling the Potential of AI for Nanomaterial Morphology Prediction

Ivan Dubrovsky, Andrei Dmitrenko, Aleksey Dmitrenko et al.

ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Robotics

Letian Chen, Nina Moorman, Matthew Gombolay

Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting

Sunny Sanyal, Hayden Prairie, Rudrajit Das et al.

Listening to the noise: Blind Denoising with Gibbs Diffusion

David Heurtel-Depeiges, Charles Margossian, Ruben Ohana et al.

ArrayDPS: Unsupervised Blind Speech Separation with a Diffusion Prior

Zhongweiyang Xu, Xulin Fan, Zhong-Qiu Wang et al.

Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback

Qiwei Di, Jiafan He, Quanquan Gu

Low-distortion and GPU-compatible Tree Embeddings in Hyperbolic Space

Max van Spengler, Pascal Mettes

Steerable Transformers for Volumetric Data

Soumyabrata Kundu, Risi Kondor

Trajectory World Models for Heterogeneous Environments

Shaofeng Yin, Jialong Wu, Siqiao Huang et al.

X-Hacking: The Threat of Misguided AutoML

Rahul Sharma, Sumantrak Mukherjee, Andrea Šipka et al.

Energy-Based Flow Matching for Generating 3D Molecular Structure

Wenyin Zhou, Christopher I Sprague, Vsevolod Viliuga et al.

PolyConf: Unlocking Polymer Conformation Generation through Hierarchical Generative Models

Fanmeng Wang, Wentao Guo, Qi Ou et al.

Machines and Mathematical Mutations: Using GNNs to Characterize Quiver Mutation Classes

Jesse He, Helen Jenne, Herman Chau et al.

Position: AI Scaling: From Up to Down and Out

Yunke Wang, Yanxi Li, Chang Xu

Position: Scaling LLM Agents Requires Asymptotic Analysis with LLM Primitives

Elliot Meyerson, Xin Qiu

Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States

Noam Razin, Yotam Alexander, Edo Cohen-Karlik et al.

Locally Interdependent Multi-Agent MDP: Theoretical Framework for Decentralized Agents with Dynamic Dependencies

Alex DeWeese, Guannan Qu

CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation

Minghao Fu, Guo-Hua Wang, Liangfu Cao et al.

Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations

Justin Deschenaux, Igor Krawczuk, Grigorios Chrysos et al.

Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts

Shengzhuang Chen, Jihoon Tack, Yunqiao Yang et al.

PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-Performer

Chang Chen, Junyeob Baek, Fei Deng et al.

Solving Probabilistic Verification Problems of Neural Networks using Branch and Bound

David Boetius, Stefan Leue, Tobias Sutter

Deconstructing the Goldilocks Zone of Neural Network Initialization

Artem Vysogorets, Anna Dawid, Julia Kempe

Point-Level Topological Representation Learning on Point Clouds

Vincent P. Grande, Michael Schaub

When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series

Min-Yeong Park, Won-Jeong Lee, Seong Tae Kim et al.

A Fixed-Point Approach for Causal Generative Modeling

Meyer Scetbon, Joel Jennings, Agrin Hilmkil et al.

Local Manifold Approximation and Projection for Manifold-Aware Diffusion Planning

Kyowoon Lee, Jaesik Choi

SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity

Samir Khaki, Xiuyu Li, Junxian Guo et al.

Position: Lifetime tuning is incompatible with continual reinforcement learning

Golnaz Mesbahi, Parham Mohammad Panahi, Olya Mastikhina et al.

Emotional Face-to-Speech

Jiaxin Ye, Boyuan Cao, Hongming Shan

From Logits to Hierarchies: Hierarchical Clustering made Simple

Emanuele Palumbo, Moritz Vandenhirtz, Alain Ryser et al.

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions

Yihao Xue, Jiping Li, Baharan Mirzasoleiman

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting

Abdelhakim Benechehab, Vasilii Feofanov, Giuseppe Paolo et al.

MAGELLAN: Metacognitive predictions of learning progress guide autotelic LLM agents in large goal spaces

Loris Gaven, Thomas Carta, Clément Romac et al.

Faster Global Minimum Cut with Predictions

Helia Niaparast, Benjamin Moseley, Karan Singh

Improving the Statistical Efficiency of Cross-Conformal Prediction

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model

Timo Kaiser, Thomas Norrenbrock, Bodo Rosenhahn

Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles

Bhrij Patel, Wesley A. Suttle, Alec Koppel et al.

Conformal Prediction as Bayesian Quadrature

Jake Snell, Thomas Griffiths

TRACE Back from the Future: A Probabilistic Reasoning Approach to Controllable Language Generation

Gwen Yidou-Weng, Benjie Wang, Guy Van den Broeck

Quantum Theory and Application of Contextual Optimal Transport

Nicola Mariella, Albert Akhriev, Francesco Tacchino et al.

Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction

Ruben Weitzman, Peter Mørch Groth, Lood van Niekerk et al.

Covered Forest: Fine-grained generalization analysis of graph neural networks

Antonis Vasileiou, Ben Finkelshtein, Floris Geerts et al.

Dendritic Localized Learning: Toward Biologically Plausible Algorithm

Changze Lv, Jingwen Xu, Yiyang Lu et al.

How Flawed Is ECE? An Analysis via Logit Smoothing

Muthu Chidambaram, Holden Lee, Colin McSwiggen et al.

FAB-PPI: Frequentist, Assisted by Bayes, Prediction-Powered Inference

Stefano Cortinovis, Francois Caron

Automatic Reward Shaping from Confounded Offline Data

Mingxuan Li, Junzhe Zhang, Elias Bareinboim

Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification

Flavio Petruzzellis, Cristina Cornelio, Pietro Lió

Generalization Analysis for Supervised Contrastive Representation Learning under Non-IID Settings

Minh Hieu Nong, Antoine Ledent

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks

Laines Schmalwasser, Niklas Penzel, Joachim Denzler et al.

Regression for the Mean: Auto-Evaluation and Inference with Few Labels through Post-hoc Regression

Benjamin Eyre, David Madras

Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach

Jin Zhu, Jingyi Li, Hongyi Zhou et al.

Feedforward Few-shot Species Range Estimation

Christian Lange, Max Hamilton, Elijah Cole et al.

Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data

Mohammad Hosseini, Maryam Shanechi

diff History for Neural Language Agents

Ulyana Piterbarg, Lerrel Pinto, Rob Fergus

TokenSwift: Lossless Acceleration of Ultra Long Sequence Generation

Tong Wu, Junzhe Shen, Zixia Jia et al.

Mimicking Better by Matching the Approximate Action Distribution

Joao A. Candido Ramos, Lionel Blondé, Naoya Takeishi et al.

Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank

Mouxiang Chen, Chenghao Liu, Zemin Liu et al.

Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts

Mateo Espinosa Zarlenga, Gabriele Dominici, Pietro Barbiero et al.

Slicing Mutual Information Generalization Bounds for Neural Networks

Kimia Nadjahi, Kristjan Greenewald, Rickard Gabrielsson et al.

Graph-Based Algorithms for Diverse Similarity Search

Piyush Anand, Piotr Indyk, Ravishankar Krishnaswamy et al.

Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting

Hongbi ZHOU, Zhangkai NI

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings

Rong-Xi Tan, Ming Chen, Ke Xue et al.

The Merit of River Network Topology for Neural Flood Forecasting

Nikolas Kirschstein, Yixuan Sun

Hierarchical Masked Autoregressive Models with Low-Resolution Token Pivots

Guangting Zheng, Yehao Li, Yingwei Pan et al.

Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy

Wei-Ning Chen, Berivan Isik, Peter Kairouz et al.

Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning

Rongzhe Wei, Mufei Li, Mohsen Ghassemi et al.

On the Local Complexity of Linear Regions in Deep ReLU Networks

Niket Patel, Guido Montufar

CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection

Karish Grover, Geoff Gordon, Christos Faloutsos

Task-Agnostic Pre-training and Task-Guided Fine-tuning for Versatile Diffusion Planner

Chenyou Fan, Chenjia Bai, Zhao Shan et al.

Token Coordinated Prompt Attention is Needed for Visual Prompting

Zichen Liu, Xu Zou, Gang Hua et al.

Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need

Shangda Yang, Vitaly Zankin, Maximilian Balandat et al.

CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition

Zebin Wang, Menghan Lin, Bolin Shen et al.

Wasserstein Policy Optimization

David Pfau, Ian Davies, Diana Borsa et al.

Representative Ranking for Deliberation in the Public Sphere

Manon Revel, Smitha Milli, Tyler Lu et al.

Position: A Theory of Deep Learning Must Include Compositional Sparsity

David A. Danhofer, Davide DAscenzo, Rafael Dubach et al.

Disparate Impact on Group Accuracy of Linearization for Private Inference

Saswat Das, Marco Romanelli, Ferdinando Fioretto

Enhancing Cross-Modal Fine-Tuning with Gradually Intermediate Modality Generation

Lincan Cai, Shuang Li, Wenxuan Ma et al.

Understanding Model Ensemble in Transferable Adversarial Attack

Wei Yao, Zeliang Zhang, Huayi Tang et al.

ADHMR: Aligning Diffusion-based Human Mesh Recovery via Direct Preference Optimization

Wenhao Shen, Wanqi Yin, Xiaofeng Yang et al.

Best of Both Worlds Guarantees for Smoothed Online Quadratic Optimization

Neelkamal Bhuyan, Debankur Mukherjee, Adam Wierman

Position: Contextual Integrity is Inadequately Applied to Language Models

Yan Shvartzshnaider, Vasisht Duddu

TGDPO: Harnessing Token-Level Reward Guidance for Enhancing Direct Preference Optimization

Mingkang Zhu, Xi Chen, Zhongdao Wang et al.

Distributionally Robust Policy Learning under Concept Drifts

Jingyuan Wang, Zhimei Ren, Ruohan Zhan et al.

Certification for Differentially Private Prediction in Gradient-Based Training

Matthew Wicker, Philip Sosnin, Igor Shilov et al.

Standardized Interpretable Fairness Measures for Continuous Risk Scores

Ann-Kristin Becker, Oana Dumitrasc, Klaus Broelemann

Sub-Sequential Physics-Informed Learning with State Space Model

Chenhui Xu, Dancheng Liu, Yuting Hu et al.

Neural-Kernel Conditional Mean Embeddings

Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic

Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance

Chiraag Kaushik, Ran Liu, Chi-Heng Lin et al.

Provably Efficient RL for Linear MDPs under Instantaneous Safety Constraints in Non-Convex Feature Spaces

Amirhossein Roknilamouki, Arnob Ghosh, Ming Shi et al.

PANDAS: Improving Many-shot Jailbreaking via Positive Affirmation, Negative Demonstration, and Adaptive Sampling

Avery Ma, Yangchen Pan, Amir-massoud Farahmand

FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for Enabling Fair LLM-Based Recommender Systems

Arya Fayyazi, Mehdi Kamal, Massoud Pedram

Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models

Huanjian Zhou, Masashi Sugiyama

Policy Design for Two-sided Platforms with Participation Dynamics

Haruka Kiyohara, Fan Yao, Sarah Dean

Discrepancies are Virtue: Weak-to-Strong Generalization through Lens of Intrinsic Dimension

Yijun Dong, Yicheng Li, Yunai Li et al.

Matryoshka Quantization

Pranav Nair, Puranjay Datta, Jeff Dean et al.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need

Ziming Hong, Runnan Chen, Zengmao Wang et al.

When can in-context learning generalize out of task distribution?

Chase Goddard, Lindsay Smith, Wave Ngampruetikorn et al.

Incorporating Arbitrary Matrix Group Equivariance into KANs

Lexiang Hu, Yisen Wang, Zhouchen Lin

The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm

Giseung Park, woohyeon Byeon, Seongmin Kim et al.

LPGD: A General Framework for Backpropagation through Embedded Optimization Layers

Anselm Paulus, Georg Martius, Vit Musil

Linear $Q$-Learning Does Not Diverge in $L^2$: Convergence Rates to a Bounded Set

Xinyu Liu, Zixuan Xie, Shangtong Zhang

Solving Hierarchical Information-Sharing Dec-POMDPs: An Extensive-Form Game Approach

Johan Peralez, Aurélien Delage, Olivier Buffet et al.

Score-Based Diffusion Policy Compatible with Reinforcement Learning via Optimal Transport

Mingyang Sun, Pengxiang Ding, Weinan Zhang et al.

Learning to Remove Cuts in Integer Linear Programming

Pol Puigdemont, EFSTRATIOS PANTELEIMON SKOULAKIS, Grigorios Chrysos et al.

Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints

Dan Qiao, Yu-Xiang Wang