Microsoft at ICML 2023

Understanding Int4 Quantization for Language Models: Latency Speedup, Composability, and Failure Cases

Xiaoxia Wu, Cheng Li, Reza Yazdani Aminabadi, Zhewei Yao, Yuxiong He

Global Selection of Contrastive Batches via Optimization on Sample Permutations

Vin Sachidananda, Ziyi Yang, Chenguang Zhu

Infinite Action Contextual Bandits with Reusable Data Exhaust

Mark Rucker, Yinglun Zhu, Paul Mineiro

Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games

Dylan Foster, Noah Golowich, Sham M. Kakade

Retrosynthetic Planning with Dual Value Networks

Guoqing Liu, Di Xue, Shufang Xie, Yingce Xia, Austin Tripp, Krzysztof Maziarz, Marwin Segler, Tao Qin, Zongzhang Zhang, Tie-Yan Liu

Geometric Clifford Algebra Networks

David Ruhe, Jayesh K. Gupta, Steven de Keninck, Max Welling, Johannes Brandstetter

Hindsight Learning for MDPs with Exogenous Inputs

Sean R. Sinclair, Felipe Vieira Frujeri, Ching-An Cheng, Luke Marshall, Hugo Barbalho, Jingling Li, Jennifer Neville, Ishai Menache, Adith Swaminathan

MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations

Anqi Li, Byron Boots, Ching-An Cheng

Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards

Yulian Wu, Xingyu Zhou, Sayak Ray Chowdhury, Di Wang

Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies

Gati Aher, Rosa I. Arriaga, Adam Tauman Kalai

InfoOT: Information Maximizing Optimal Transport

Ching-Yao Chuang, Stefanie Jegelka, David Alvarez-Melis

Principled Offline RL with Rich Exogenous Information

Riashat Islam, Manan Tomar, Alex Lamb, Yonathan Efroni, Hongyu Zang, Aniket Didolkar, Dipendra Misra, Xin Li, Harm van Seijen, Remi Tachet des Combes, John Langford

ClimaX: A foundation model for weather and climate

Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K. Gupta, Aditya Grover

Rigid body flows for sampling molecular crystal structures

Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé

Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL

Zakaria Mhammedi, Dylan Foster, Alexander Rakhlin

HyperTuning: Toward Adapting Large Language Models without Back-propagation

Jason Phang, Yi Mao, Pengcheng He, Weizhu Chen

Differentiable Multi-Target Causal Bayesian Experimental Design

Yashas Annadani, Panagiotis Tigas, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer

X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion

Hanqing Zhao, Dianmo Sheng, Jianmin Bao, Dongdong Chen, Dong Chen, Fang Wen, Lu Yuan, Ce Liu, Wenbo Zhou, Qi Chu, Weiming Zhang, Nenghai Yu

On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology

Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lió, Michael Bronstein

NeuralStagger: accelerating physics-constrained neural PDE solver with spatial-temporal decomposition

Xinquan Huang, Wenlei Shi, Qi Meng, Yue Wang, Xiaotian Gao, Jia Zhang, Tie-Yan Liu

Less is More: Task-aware Layer-wise Distillation for Language Model Compression

Chen Liang, Simiao Zuo, Qingru Zhang, Pengcheng He, Weizhu Chen, Tuo Zhao

Fairness in Streaming Submodular Maximization over a Matroid Constraint

Marwa El Halabi, Federico Fusco, Ashkan Norouzi-Fard, Jakab Tardos, Jakub Tarnawski

Bandit Multi-linear DR-Submodular Maximization and Its Applications on Adversarial Submodular Bandits

Zongqi Wan, Jialin Zhang, Wei Chen, Xiaoming Sun, Zhijie Zhang

Width and Depth Limits Commute in Residual Networks

Soufiane Hayou, Greg Yang

Differentiable Tree Operations Promote Compositional Generalization

Paul Soulos, Edward Hu, Kate McCurdy, Yunmo Chen, Roland Fernandez, Paul Smolensky, Jianfeng Gao

LongCoder: A Long-Range Pre-trained Language Model for Code Completion

Daya Guo, Canwen Xu, Nan Duan, Jian Yin, Julian McAuley

LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation

Yixiao Li, Yifan Yu, Qingru Zhang, Chen Liang, Pengcheng He, Weizhu Chen, Tuo Zhao

Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling

Adam Bouland, Yosheb Getachew, Yujia Jin, Aaron Sidford, Kevin Tian

Target-based Surrogates for Stochastic Optimization

Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Nicolas Le Roux

Contextual Combinatorial Bandits with Probabilistically Triggered Arms

Xutong Liu, Jinhang Zuo, Siwei Wang, John C.S. Lui, Mohammad Hajiesmaili, Adam Wierman, Wei Chen

Semi-Offline Reinforcement Learning for Optimized Text Generation

Changyu Chen, Xiting Wang, Yiqiao Jin, Victor Ye Dong, Li Dong, Jim Cao, Yi Liu, Rui Yan

Provable Reset-free Reinforcement Learning by No-Regret Reduction

Hoai-An Nguyen, Ching-An Cheng

On the Expressive Power of Geometric Graph Neural Networks

Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Liò

Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies

Gati Aher, Rosa I. Arriaga, Adam Tauman Kalai

BEATs: Audio Pre-Training with Acoustic Tokenizers

Sanyuan Chen, Yu Wu, Chengyi Wang, Shujie Liu, Daniel Tompkins, Zhuo Chen, Furu Wei

BEATs: Audio Pre-Training with Acoustic Tokenizers

Sanyuan Chen, Yu Wu, Chengyi Wang, Shujie Liu, Daniel Tompkins, Zhuo Chen, Furu Wei

OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models

Enshu Liu, Xuefei Ning, Zinan Lin, Huazhong Yang, Yu Wang

Mitigating Spurious Correlations in Multi-modal Models during Fine-tuning

Yu Yang, Besmira Nushi, Hamid Palangi, Baharan Mirzasoleiman

CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design

Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster

Magneto: A Foundation Transformer

Hongyu Wang, Shuming Ma, Shaohan Huang, Li Dong, Wenhui Wang, Zhiliang Peng, Yu Wu, Payal Bajaj, Saksham Singhal, Alon Benhaim, Barun Patra, Zhun Liu, Vishrav Chaudhary, Xia Song, Furu Wei

Statistical Learning under Heterogenous Distribution Shift

Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy

Personalized Federated Learning with Inferred Collaboration Graphs

Rui Ye, Zhenyang Ni, Fangzhao Wu, Siheng Chen, Yan-Feng Wang

NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation

Jianfeng Wang, Daniela Massiceti, Xiaolin Hu, Vladimir Pavlovic, Thomas Lukasiewicz

Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space

Michael Diao, Krishnakumar Balasubramanian, Sinho Chewi, Adil Salim

Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models

Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, Weizhu Chen

Text Generation with Diffusion Language Models: A Pre-training Approach with Continuous Paragraph Denoise

Zhenghao Lin, Yeyun Gong, Yelong Shen, Tong Wu, Zhihao Fan, Chen Lin, Nan Duan, Weizhu Chen

Meta Optimal Transport

Brandon Amos, Samuel Cohen, Giulia Luise, Ievgen Redko

Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL

Zakaria Mhammedi, Dylan Foster, Alexander Rakhlin

Learning Instance-Specific Augmentations by Capturing Local Invariances

Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim

Bayesian Estimation of Differential Privacy

Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Ruehle, Andrew Paverd, Mohammad Naseri, Boris Köpf, Daniel Jones

Streaming Active Learning with Deep Neural Networks

Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan Ash