ICLR 2024

2,260 papers

Provable Robust Watermarking for AI-Generated Text

Xuandong Zhao, Prabhanjan Vijendra Ananth, Lei Li, Yu-Xiang Wang

Principled Architecture-aware Scaling of Hyperparameters

Wuyang Chen, Junru Wu, Zhangyang Wang, Boris Hanin

CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception

Jiachen Sun, Haizhong Zheng, Qingzhao Zhang, Atul Prakash, Zhuoqing Mao

Language Control Diffusion: Efficiently Scaling through Space, Time, and Tasks

Edwin Zhang, Yujie Lu, Shinda Huang, William Yang Wang, Amy Zhang

CellPLM: Pre-training of Cell Language Model Beyond Single Cells

Hongzhi Wen, Wenzhuo Tang, Xinnan Dai, Jiayuan Ding, Wei Jin

Federated Causal Discovery from Heterogeneous Data

Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen

DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion Models

Sohyun An, Hayeon Lee, Jaehyeong Jo, Seanie Lee, Sung Ju Hwang

Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time

Yuzhou Gu, Zhao Song, Junze Yin, Lichen Zhang

Implicit bias of SGD in L_2-regularized linear DNNs: One-way jumps from high to low rank

Zihan Wang, Arthur Jacot

Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features

Annie S Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn

Reasoning with Latent Diffusion in Offline Reinforcement Learning

Siddarth Venkatraman, Shivesh Khaitan, Ravi Tej Akella, John Dolan, Jeff Schneider

The Hidden Language of Diffusion Models

Hila Chefer, Oran Lang, Mor Geva, Volodymyr Polosukhin, Assaf Shocher

Self-Consuming Generative Models Go MAD

Sina Alemohammad, Josue Casco-Rodriguez, Lorenzo Luzi, Ahmed Imtiaz Humayun, Hossein Babaei

Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning

Yavuz Faruk Bakman, Duygu Nur Yaldiz, Yahya H. Ezzeldin, Salman Avestimehr

MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods

Mara Finkelstein, Markus Freitag

Adaptive Sharpness-Aware Pruning for Robust Sparse Networks

Anna Bair, Hongxu Yin, Maying Shen, Pavlo Molchanov, Jose M. Alvarez

OpenTab: Advancing Large Language Models as Open-domain Table Reasoners

Kezhi Kong, Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Chuan Lei

Neurosymbolic Grounding for Compositional World Models

Atharva Sehgal, Arya Grayeli, Jennifer J. Sun, Swarat Chaudhuri

Entropy Coding of Unordered Data Structures

Julius Kunze, Daniel Severo, Giulio Zani, Jan-Willem van de Meent, James Townsend

Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oracles

Zhiwei Tang, Dmitry Rybin, Tsung-Hui Chang

RLCD: Reinforcement Learning from Contrastive Distillation for LM Alignment

Kevin Yang, Dan Klein, Asli Celikyilmaz, Nanyun Peng, Yuandong Tian

Deep Neural Networks Tend To Extrapolate Predictably

Katie Kang, Amrith Setlur, Claire Tomlin, Sergey Levine

Conditional Information Bottleneck Approach for Time Series Imputation

MinGyu Choi, Changhee Lee

Causal-StoNet: Causal Inference for High-Dimensional Complex Data

Yaxin Fang, Faming Liang

Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization

Hanmin Li, Avetik Karagulyan, Peter Richtarik

On the Hardness of Constrained Cooperative Multi-Agent Reinforcement Learning

Ziyi Chen, Yi Zhou, Heng Huang

Views Can Be Deceiving: Improved SSL Through Feature Space Augmentation

Kimia Hamidieh, Haoran Zhang, Swami Sankaranarayanan, Marzyeh Ghassemi

Federated Text-driven Prompt Generation for Vision-Language Models

Chen Qiu, Xingyu Li, Chaithanya Kumar Mummadi, Madan Ravi Ganesh, Zhenzhen Li

A Lie Group Approach to Riemannian Batch Normalization

Ziheng Chen, Yue Song, Yunmei Liu, Nicu Sebe

Time Fairness in Online Knapsack Problems

Adam Lechowicz, Rik Sengupta, Bo Sun, Shahin Kamali, Mohammad Hajiesmaili

GOAt: Explaining Graph Neural Networks via Graph Output Attribution

Shengyao Lu, Keith G. Mills, Jiao He, Bang Liu, Di Niu

Listen, Think, and Understand

Yuan Gong, Hongyin Luo, Alexander H. Liu, Leonid Karlinsky, James R. Glass

Aligning Relational Learning with Lipschitz Fairness

Yaning Jia, Chunhui Zhang, Soroush Vosoughi

Fair Classifiers that Abstain without Harm

Tongxin Yin, Jean-Francois Ton, Ruocheng Guo, Yuanshun Yao, mingyan liu

DrS: Learning Reusable Dense Rewards for Multi-Stage Tasks

Tongzhou Mu, Minghua Liu, Hao Su

A Dynamical View of the Question of Why

Mehdi Fatemi, Sindhu C. M. Gowda

Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions

Xufeng Cai, Ahmet Alacaoglu, Jelena Diakonikolas

ReMasker: Imputing Tabular Data with Masked Autoencoding

Tianyu Du, Luca Melis, Ting Wang

Teaching Arithmetic to Small Transformers

Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, Kangwook Lee, Dimitris Papailiopoulos

MetaPhysiCa: Improving OOD Robustness in Physics-informed Machine Learning

S Chandra Mouli, Muhammad Alam, Bruno Ribeiro

AUC-CL: A Batchsize-Robust Framework for Self-Supervised Contrastive Representation Learning

Rohan Sharma, Kaiyi Ji, Zhiqiang Xu, Changyou Chen

LabelDP-Pro: Learning with Label Differential Privacy via Projections

Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models

Yangming Li, Boris van Breugel, Mihaela van der Schaar

The Update-Equivalence Framework for Decision-Time Planning

Samuel Sokota, Gabriele Farina, David J Wu, Hengyuan Hu, Kevin A. Wang

On Error Propagation of Diffusion Models

Yangming Li, Mihaela van der Schaar

CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?

Ibrahim Alabdulmohsin, Xiao Wang, Andreas Peter Steiner, Priya Goyal, Alexander D'Amour

Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND

Qiyu Kang, KAI ZHAO, Qinxu Ding, Feng Ji, Xuhao Li

Multi-Resolution Diffusion Models for Time Series Forecasting

Lifeng Shen, Weiyu Chen, James Kwok

DeepZero: Scaling Up Zeroth-Order Optimization for Deep Model Training

Aochuan Chen, Yimeng Zhang, Jinghan Jia, James Diffenderfer, Konstantinos Parasyris

The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction

Pratyusha Sharma, Jordan T. Ash, Dipendra Misra

Adaptive Retrieval and Scalable Indexing for k-NN Search with Cross-Encoders

Nishant Yadav, Nicholas Monath, Manzil Zaheer, Rob Fergus, Andrew McCallum

Provable Reward-Agnostic Preference-Based Reinforcement Learning

Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee

Multimodal Learning Without Labeled Multimodal Data: Guarantees and Applications

Paul Pu Liang, Chun Kai Ling, Yun Cheng, Alexander Obolenskiy, Yudong Liu

Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory

Niloofar Mireshghallah, Hyunwoo Kim, Xuhui Zhou, Yulia Tsvetkov, Maarten Sap

Provable Offline Preference-Based Reinforcement Learning

Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun

Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks

Ziping Xu, Zifan Xu, Runxuan Jiang, Peter Stone, Ambuj Tewari

JoMA: Demystifying Multilayer Transformers via Joint Dynamics of MLP and Attention

Yuandong Tian, Yiping Wang, Zhenyu Zhang, Beidi Chen, Simon Shaolei Du

Robustifying State-space Models for Long Sequences via Approximate Diagonalization

Annan Yu, Arnur Nigmetov, Dmitriy Morozov, Michael W. Mahoney, N. Benjamin Erichson

Understanding when Dynamics-Invariant Data Augmentations Benefit Model-free Reinforcement Learning Updates

Nicholas Corrado, Josiah P. Hanna

Multimodal Molecular Pretraining via Modality Blending

Qiying Yu, Yudi Zhang, Yuyan Ni, Shikun Feng, Yanyan Lan

Learning with Language-Guided State Abstractions

Andi Peng, Ilia Sucholutsky, Belinda Z. Li, Theodore Sumers, Thomas L. Griffiths

ReLoRA: High-Rank Training Through Low-Rank Updates

Vladislav Lialin, Sherin Muckatira, Namrata Shivagunde, Anna Rumshisky

Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model

Karsten Roth, Lukas Thede, A. Sophia Koepke, Oriol Vinyals, Olivier J Henaff

Enabling Lanuguage Models to Implicitly Learn Self-Improvement

Ziqi Wang, Le Hou, Tianjian Lu, Yuexin Wu, Yunxuan Li

Teaching Language Models to Hallucinate Less with Synthetic Tasks

Erik Jones, Hamid Palangi, Clarisse Simões Ribeiro, Varun Chandrasekaran, Subhabrata Mukherjee

Neural Neighborhood Search for Multi-agent Path Finding

Zhongxia Yan, Cathy Wu

Generative Human Motion Stylization in Latent Space

Chuan Guo, Yuxuan Mu, Xinxin Zuo, Peng Dai, Youliang Yan

On Differentially Private Federated Linear Contextual Bandits

Xingyu Zhou, Sayak Ray Chowdhury

Motif: Intrinsic Motivation from Artificial Intelligence Feedback

Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon

Guess & Sketch: Language Model Guided Transpilation

Celine Lee, Abdulrahman Mahmoud, Michal Kurek, Simone Campanoni, David Brooks

Towards Optimal Feature-Shaping Methods for Out-of-Distribution Detection

Qinyu Zhao, Ming Xu, Kartik Gupta, Akshay Asthana, Liang Zheng

Scalable Diffusion for Materials Generation

Sherry Yang, KwangHwan Cho, Amil Merchant, Pieter Abbeel, Dale Schuurmans

SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training

Kazem Meidani, Parshin Shojaee, Chandan K. Reddy, Amir Barati Farimani

Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian distributions

Frank Cole, Yulong Lu

Faithful Rule Extraction for Differentiable Rule Learning Models

Xiaxia Wang, David Jaime Tena Cucala, Bernardo Cuenca Grau, Ian Horrocks

Horizon-Free Regret for Linear Markov Decision Processes

Zihan Zhang, Jason D. Lee, Yuxin Chen, Simon Shaolei Du

Probabilistic Adaptation of Black-Box Text-to-Video Models

Sherry Yang, Yilun Du, Bo Dai, Dale Schuurmans, Joshua B. Tenenbaum

Smooth ECE: Principled Reliability Diagrams via Kernel Smoothing

Jaroslaw Blasiok, Preetum Nakkiran

GIO: Gradient Information Optimization for Training Dataset Selection

Dante Everaert, Christopher Potts

Empirical Likelihood for Fair Classification

Pangpang Liu, Yichuan Zhao

LDReg: Local Dimensionality Regularized Self-Supervised Learning

Hanxun Huang, Ricardo J. G. B. Campello, Sarah Monazam Erfani, Xingjun Ma, Michael E. Houle

Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models

Pablo Pernias, Dominic Rampas, Mats Leon Richter, Christopher Pal, Marc Aubreville

FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Tri Dao

Improving Intrinsic Exploration by Creating Stationary Objectives

Roger Creus Castanyer, Joshua Romoff, Glen Berseth

Modulate Your Spectrum in Self-Supervised Learning

Xi Weng, Yunhao Ni, Tengwei Song, Jie Luo, Rao Muhammad Anwer

DATS: Difficulty-Aware Task Sampler for Meta-Learning Physics-Informed Neural Networks

Maryam Toloubidokhti, Yubo Ye, Ryan Missel, Xiajun Jiang, Nilesh Kumar

Ghost on the Shell: An Expressive Representation of General 3D Shapes

Zhen Liu, Yao Feng, Yuliang Xiu, Weiyang Liu, Liam Paull

Dynamic Discounted Counterfactual Regret Minimization

Hang Xu, Kai Li, Haobo Fu, Qiang Fu, Junliang Xing

SemiReward: A General Reward Model for Semi-supervised Learning

Siyuan Li, Weiyang Jin, Zedong Wang, Fang Wu, Zicheng Liu

Future Language Modeling from Temporal Document History

Changmao Li, Jeffrey Flanigan

Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models

Shuai Zhao, Xiaohan Wang, Linchao Zhu, Yi Yang

Identifiable Latent Polynomial Causal Models through the Lens of Change

Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang

Federated Recommendation with Additive Personalization

Zhiwei Li, Guodong Long, Tianyi Zhou

Achieving Human Parity in Content-Grounded Datasets Generation

Asaf Yehudai, Boaz Carmeli, Yosi Mass, Ofir Arviv, Nathaniel Mills

Teaching Large Language Models to Self-Debug

Xinyun Chen, Maxwell Lin, Nathanael Schärli, Denny Zhou

Flow Matching on General Geometries

Ricky T. Q. Chen, Yaron Lipman

A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables

Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng

Magnitude Invariant Parametrizations Improve Hypernetwork Learning

Jose Javier Gonzalez Ortiz, John Guttag, Adrian V Dalca

Fusing Models with Complementary Expertise

Hongyi Wang, Felipe Maia Polo, Yuekai Sun, Souvik Kundu, Eric Xing

Improving Convergence and Generalization Using Parameter Symmetries

Bo Zhao, Robert M. Gower, Robin Walters, Rose Yu