Subgradient Descent Learns Orthogonal Dictionaries. - Yu Bai 017; Qijia Jiang; Ju Sun (ICLR (Poster), 2019)

Approximability of Discriminators Implies Diversity in GANs. - Yu Bai 017; Tengyu Ma 001; Andrej Risteski (ICLR (Poster), 2019)

ProxQuant: Quantized Neural Networks via Proximal Operators. - Yu Bai 017; Yu-Xiang Wang 003; Edo Liberty (ICLR (Poster), 2019)

Provably Efficient Q-Learning with Low Switching Cost. - Yu Bai 017; Tengyang Xie; Nan Jiang 008; Yu-Xiang Wang 003 (NeurIPS, 2019)

Proximal algorithms for constrained composite optimization, with applications to solving low-rank SDPs. - Yu Bai 017; John C. Duchi; Song Mei (CoRR, 2019)

Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks. - Yu Bai 017; Jason D. Lee (ICLR, 2020)

Provable Self-Play Algorithms for Competitive Reinforcement Learning. - Yu Bai 017; Chi Jin 001 (ICML, 2020)

Near-Optimal Reinforcement Learning with Self-Play. - Yu Bai 017; Chi Jin 001; Tiancheng Yu (NeurIPS, 2020)

Towards Understanding Hierarchical Learning: Benefits of Neural Representations. - Minshuo Chen; Yu Bai 017; Jason D. Lee; Tuo Zhao; Huan Wang 014; Caiming Xiong; Richard Socher (NeurIPS, 2020)

Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width. - Yu Bai 017; Ben Krause; Huan Wang 014; Caiming Xiong; Richard Socher (CoRR, 2020)

Near Optimal Provable Uniform Convergence in Off-Policy Evaluation for Reinforcement Learning. - Ming Yin 003; Yu Bai 017; Yu-Xiang Wang 003 (CoRR, 2020)

Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning. - Ming Yin 003; Yu Bai 017; Yu-Xiang Wang 003 (AISTATS, 2021)

How Important is the Train-Validation Split in Meta-Learning? - Yu Bai 017; Minshuo Chen; Pan Zhou 002; Tuo Zhao; Jason D. Lee; Sham M. Kakade; Huan Wang 014; Caiming Xiong (ICML, 2021)

Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification. - Yu Bai 017; Song Mei; Huan Wang 014; Caiming Xiong (ICML, 2021)

A Sharp Analysis of Model-based Reinforcement Learning with Self-Play. - Qinghua Liu; Tiancheng Yu; Yu Bai 017; Chi Jin 001 (ICML, 2021)

Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models. - Zitong Yang; Yu Bai 017; Song Mei (ICML, 2021)

Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games. - Yu Bai 017; Chi Jin 001; Huan Wang 014; Caiming Xiong (NeurIPS, 2021)

Understanding the Under-Coverage Bias in Uncertainty Estimation. - Yu Bai 017; Song Mei; Huan Wang 014; Caiming Xiong (NeurIPS, 2021)

Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning. - Tengyang Xie; Nan Jiang 008; Huan Wang 014; Caiming Xiong; Yu Bai 017 (NeurIPS, 2021)

Near-Optimal Offline Reinforcement Learning via Double Variance Reduction. - Ming Yin 003; Yu Bai 017; Yu-Xiang Wang 003 (NeurIPS, 2021)

Localized Calibration: Metrics and Recalibration. - Rachel Luo; Aadyot Bhatnagar; Huan Wang 014; Caiming Xiong; Silvio Savarese; Yu Bai 017; Shengjia Zhao; Stefano Ermon (CoRR, 2021)

Conformal Predictor for Improving Zero-Shot Text Classification Efficiency. - Prafulla Kumar Choubey; Yu Bai 017; Chien-Sheng Wu; Wenhao Liu 003; Nazneen Rajani (EMNLP, 2022)

Efficient and Differentiable Conformal Prediction with General Function Classes. - Yu Bai 017; Song Mei; Huan Wang 014; Yingbo Zhou; Caiming Xiong (ICLR, 2022)

When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently? - Ziang Song; Song Mei; Yu Bai 017 (ICLR, 2022)

Near-Optimal Learning of Extensive-Form Games with Imperfect Information. - Yu Bai 017; Chi Jin 001; Song Mei; Tiancheng Yu (ICML, 2022)

Efficient Phi-Regret Minimization in Extensive-Form Games via Online Mirror Descent. - Yu Bai 017; Chi Jin 001; Song Mei; Ziang Song; Tiancheng Yu (NeurIPS, 2022)

Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials. - Eshaan Nichani; Yu Bai 017; Jason D. Lee (NeurIPS, 2022)

Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games. - Ziang Song; Song Mei; Yu Bai 017 (NeurIPS, 2022)

Policy Optimization for Markov Games: Unified Framework and Faster Convergence. - Runyu Zhang 001; Qinghua Liu; Huan Wang 014; Caiming Xiong; Na Li 002; Yu Bai 017 (NeurIPS, 2022)

Local calibration: metrics and recalibration. - Rachel Luo; Aadyot Bhatnagar; Yu Bai 017; Shengjia Zhao; Huan Wang 014; Caiming Xiong; Silvio Savarese; Stefano Ermon; Edward Schmerling; Marco Pavone 001 (UAI, 2022)

Efficient Φ-Regret Minimization in Extensive-Form Games via Online Mirror Descent. - Yu Bai 017; Chi Jin 001; Song Mei; Ziang Song; Tiancheng Yu (CoRR, 2022)

Unified Algorithms for RL with Decision-Estimation Coefficients: No-Regret, PAC, and Reward-Free Learning. - Fan Chen 006; Song Mei; Yu Bai 017 (CoRR, 2022)

Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation. - Yuanhao Wang 001; Qinghua Liu; Yu Bai 017; Chi Jin 001 (COLT, 2023)

Learning Rationalizable Equilibria in Multiplayer Games. - Yuanhao Wang 001; Dingwen Kong; Yu Bai 017; Chi Jin 001 (ICLR, 2023)

Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms. - Fan Chen 006; Yu Bai 017; Song Mei (ICLR, 2023)

The Role of Coverage in Online Reinforcement Learning. - Tengyang Xie; Dylan J. Foster; Yu Bai 017; Nan Jiang 008; Sham M. Kakade (ICLR, 2023)

Improved Online Conformal Prediction via Strongly Adaptive Online Learning. - Aadyot Bhatnagar; Huan Wang 014; Caiming Xiong; Yu Bai 017 (ICML, 2023)

Lower Bounds for Learning in Revealing POMDPs. - Fan Chen 006; Huan Wang 014; Caiming Xiong; Song Mei; Yu Bai 017 (ICML, 2023)

Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection. - Yu Bai 017; Fan Chen 006; Huan Wang 014; Caiming Xiong; Song Mei (NeurIPS, 2023)

Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations. - Minshuo Chen; Yu Bai 017; H. Vincent Poor; Mengdi Wang (NeurIPS, 2023)

What can a Single Attention Layer Learn? A Study Through the Random Features Lens. - Hengyu Fu; Tianyu Guo 004; Yu Bai 017; Song Mei (NeurIPS, 2023)

Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? - Lei Zhao; Mengdi Wang; Yu Bai 017 (CoRR, 2023)

How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations. - Tianyu Guo 004; Wei Hu; Song Mei; Huan Wang 014; Caiming Xiong; Silvio Savarese; Yu Bai 017 (ICLR, 2024)

Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight. - Jiacheng Guo; Minshuo Chen; Huan Wang 014; Caiming Xiong; Mengdi Wang; Yu Bai 017 (ICLR, 2024)

Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining. - Licong Lin; Yu Bai 017; Song Mei (ICLR, 2024)

Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective. - Lei Zhao; Mengdi Wang; Yu Bai 017 (ICML, 2024)

Text2Data: Low-Resource Data Generation with Textual Control. - Shiyu Wang; Yihao Feng; Tian Lan 006; Ning Yu 006; Yu Bai 017; Ran Xu 001; Huan Wang 014; Caiming Xiong; Silvio Savarese (CoRR, 2024)

Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning. - Ruiqi Zhang; Licong Lin; Yu Bai 017; Song Mei (CoRR, 2024)

Active-Dormant Attention Heads: Mechanistically Demystifying Extreme-Token Phenomena in LLMs. - Tianyu Guo 004; Druv Pai; Yu Bai 017; Jiantao Jiao; Michael I. Jordan; Song Mei (CoRR, 2024)

Efficient Reinforcement Learning With Impaired Observability: Learning to Act With Delayed and Missing State Observations. - Minshuo Chen; Jie Meng; Yu Bai 017; Yinyu Ye 001; H. Vincent Poor; Mengdi Wang (IEEE Trans. Inf. Theory, 2024)

Text2Data: Low-Resource Data Generation with Textual Control. - Shiyu Wang; Yihao Feng; Tian Lan 006; Ning Yu 006; Yu Bai 017; Ran Xu 001; Huan Wang 016; Caiming Xiong; Silvio Savarese (AAAI, 2025)

LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra. - Seth Karten; Wenzhe Li; Zihan Ding; Samuel Kleiner; Yu Bai 017; Chi Jin 001 (CoRR, 2025)