Xuezhou Zhang

papers

702

total citations

papers (10)

Neural Additive Models: Interpretable Machine Learning with Neural Nets

NEURIPS 2021arXiv

537

citations

Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks

NEURIPS 2022arXiv

citations

Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback

NEURIPS 2023arXiv

citations

Avoiding exp(R) scaling in RLHF through Preference-based Exploration

NEURIPS 2025

citations

Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization

NEURIPS 2022arXiv

citations

Efficient Reinforcement Learning in Probabilistic Reward Machines

AAAI 2025arXiv

citations

Exact Policy Recovery in Offline RL with Both Heavy-Tailed Rewards and Data Corruption

AAAI 2024

citations

Xuezhou Zhang

papers (10)

Neural Additive Models: Interpretable Machine Learning with Neural Nets

Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks

Task-agnostic Exploration in Reinforcement Learning

Provable Defense against Backdoor Policies in Reinforcement Learning

Accelerating RL for LLM Reasoning with Optimal Advantage Regression

Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback

Avoiding exp(R) scaling in RLHF through Preference-based Exploration

Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization

Efficient Reinforcement Learning in Probabilistic Reward Machines

Exact Policy Recovery in Offline RL with Both Heavy-Tailed Rewards and Data Corruption

papers (10)

Neural Additive Models: Interpretable Machine Learning with Neural Nets

Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks

Task-agnostic Exploration in Reinforcement Learning

Provable Defense against Backdoor Policies in Reinforcement Learning

Accelerating RL for LLM Reasoning with Optimal Advantage Regression

Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback

Avoiding exp(R) scaling in RLHF through Preference-based Exploration

Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization

Efficient Reinforcement Learning in Probabilistic Reward Machines

Exact Policy Recovery in Offline RL with Both Heavy-Tailed Rewards and Data Corruption