Yu Bai - RankMe

87 papers A* 35A 2Journal 47Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra. CoRR Seth Karten, Wenzhe Li, Zihan Ding, Samuel Kleiner, Yu Bai, Chi Jin
2025	A*	`conf`	Text2Data: Low-Resource Data Generation with Textual Control. AAAI Shiyu Wang, Yihao Feng, Tian Lan, Ning Yu, Yu Bai, Ran Xu, Huan Wang, Caiming Xiong, Silvio Savarese
2024	J	`jnl`	Active-Dormant Attention Heads: Mechanistically Demystifying Extreme-Token Phenomena in LLMs. CoRR Tianyu Guo, Druv Pai, Yu Bai, Jiantao Jiao, Michael I. Jordan, Song Mei
2024	J	`jnl`	Efficient Reinforcement Learning With Impaired Observability: Learning to Act With Delayed and Missing State Observations. IEEE Trans. Inf. Theory Minshuo Chen, Jie Meng, Yu Bai, Yinyu Ye, H. Vincent Poor, Mengdi Wang
2024	A*	`conf`	How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations. ICLR Tianyu Guo, Wei Hu, Song Mei, Huan Wang, Caiming Xiong, Silvio Savarese, Yu Bai
2024	A*	`conf`	Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective. ICML Lei Zhao, Mengdi Wang, Yu Bai
2024	J	`jnl`	Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning. CoRR Ruiqi Zhang, Licong Lin, Yu Bai, Song Mei
2024	A*	`conf`	Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight. ICLR Jiacheng Guo, Minshuo Chen, Huan Wang, Caiming Xiong, Mengdi Wang, Yu Bai
2024	J	`jnl`	Text2Data: Low-Resource Data Generation with Textual Control. CoRR Shiyu Wang, Yihao Feng, Tian Lan, Ning Yu, Yu Bai, Ran Xu, Huan Wang, Caiming Xiong, Silvio Savarese
2024	A*	`conf`	Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining. ICLR Licong Lin, Yu Bai, Song Mei
2023	A*	`conf`	Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation. COLT Yuanhao Wang, Qinghua Liu, Yu Bai, Chi Jin
2023	J	`jnl`	Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation. CoRR Yuanhao Wang, Qinghua Liu, Yu Bai, Chi Jin
2023	A*	`conf`	Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations. NeurIPS Minshuo Chen, Yu Bai, H. Vincent Poor, Mengdi Wang
2023	J	`jnl`	Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations. CoRR Minshuo Chen, Yu Bai, H. Vincent Poor, Mengdi Wang
2023	J	`jnl`	How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations. CoRR Tianyu Guo, Wei Hu, Song Mei, Huan Wang, Caiming Xiong, Silvio Savarese, Yu Bai
2023	A*	`conf`	Improved Online Conformal Prediction via Strongly Adaptive Online Learning. ICML Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Yu Bai
2023	J	`jnl`	Improved Online Conformal Prediction via Strongly Adaptive Online Learning. CoRR Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Yu Bai
2023	J	`jnl`	Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? CoRR Lei Zhao, Mengdi Wang, Yu Bai
2023	A*	`conf`	Learning Rationalizable Equilibria in Multiplayer Games. ICLR Yuanhao Wang, Dingwen Kong, Yu Bai, Chi Jin
2023	A*	`conf`	Lower Bounds for Learning in Revealing POMDPs. ICML Fan Chen, Huan Wang, Caiming Xiong, Song Mei, Yu Bai
2023	J	`jnl`	Lower Bounds for Learning in Revealing POMDPs. CoRR Fan Chen, Huan Wang, Caiming Xiong, Song Mei, Yu Bai
2023	A*	`conf`	Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms. ICLR Fan Chen, Yu Bai, Song Mei
2023	J	`jnl`	Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight. CoRR Jiacheng Guo, Minshuo Chen, Huan Wang, Caiming Xiong, Mengdi Wang, Yu Bai
2023	A*	`conf`	The Role of Coverage in Online Reinforcement Learning. ICLR Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade
2023	J	`jnl`	Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining. CoRR Licong Lin, Yu Bai, Song Mei
2023	A*	`conf`	Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection. NeurIPS Yu Bai, Fan Chen, Huan Wang, Caiming Xiong, Song Mei
2023	J	`jnl`	Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection. CoRR Yu Bai, Fan Chen, Huan Wang, Caiming Xiong, Song Mei
2023	A*	`conf`	What can a Single Attention Layer Learn? A Study Through the Random Features Lens. NeurIPS Hengyu Fu, Tianyu Guo, Yu Bai, Song Mei
2023	J	`jnl`	What can a Single Attention Layer Learn? A Study Through the Random Features Lens. CoRR Hengyu Fu, Tianyu Guo, Yu Bai, Song Mei
2022	A*	`conf`	Conformal Predictor for Improving Zero-Shot Text Classification Efficiency. EMNLP Prafulla Kumar Choubey, Yu Bai, Chien-Sheng Wu, Wenhao Liu, Nazneen Rajani
2022	J	`jnl`	Conformal Predictor for Improving Zero-shot Text Classification Efficiency. CoRR Prafulla Kumar Choubey, Yu Bai, Chien-Sheng Wu, Wenhao Liu, Nazneen Rajani
2022	A*	`conf`	Efficient Phi-Regret Minimization in Extensive-Form Games via Online Mirror Descent. NeurIPS Yu Bai, Chi Jin, Song Mei, Ziang Song, Tiancheng Yu
2022	A*	`conf`	Efficient and Differentiable Conformal Prediction with General Function Classes. ICLR Yu Bai, Song Mei, Huan Wang, Yingbo Zhou, Caiming Xiong
2022	J	`jnl`	Efficient and Differentiable Conformal Prediction with General Function Classes. CoRR Yu Bai, Song Mei, Huan Wang, Yingbo Zhou, Caiming Xiong
2022	J	`jnl`	Efficient Φ-Regret Minimization in Extensive-Form Games via Online Mirror Descent. CoRR Yu Bai, Chi Jin, Song Mei, Ziang Song, Tiancheng Yu
2022	A*	`conf`	Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials. NeurIPS Eshaan Nichani, Yu Bai, Jason D. Lee
2022	J	`jnl`	Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials. CoRR Eshaan Nichani, Yu Bai, Jason D. Lee
2022	J	`jnl`	Learning Rationalizable Equilibria in Multiplayer Games. CoRR Yuanhao Wang, Dingwen Kong, Yu Bai, Chi Jin
2022	A	`conf`	Local calibration: metrics and recalibration. UAI Rachel Luo, Aadyot Bhatnagar, Yu Bai, Shengjia Zhao, Huan Wang, Caiming Xiong, Silvio Savarese, Stefano Ermon, Edward Schmerling, Marco Pavone
2022	A*	`conf`	Near-Optimal Learning of Extensive-Form Games with Imperfect Information. ICML Yu Bai, Chi Jin, Song Mei, Tiancheng Yu
2022	J	`jnl`	Near-Optimal Learning of Extensive-Form Games with Imperfect Information. CoRR Yu Bai, Chi Jin, Song Mei, Tiancheng Yu
2022	J	`jnl`	Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms. CoRR Fan Chen, Yu Bai, Song Mei
2022	A*	`conf`	Policy Optimization for Markov Games: Unified Framework and Faster Convergence. NeurIPS Runyu Zhang, Qinghua Liu, Huan Wang, Caiming Xiong, Na Li, Yu Bai
2022	J	`jnl`	Policy Optimization for Markov Games: Unified Framework and Faster Convergence. CoRR Runyu Zhang, Qinghua Liu, Huan Wang, Caiming Xiong, Na Li, Yu Bai
2022	A*	`conf`	Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games. NeurIPS Ziang Song, Song Mei, Yu Bai
2022	J	`jnl`	Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games. CoRR Ziang Song, Song Mei, Yu Bai
2022	J	`jnl`	The Role of Coverage in Online Reinforcement Learning. CoRR Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade
2022	J	`jnl`	Unified Algorithms for RL with Decision-Estimation Coefficients: No-Regret, PAC, and Reward-Free Learning. CoRR Fan Chen, Song Mei, Yu Bai
2022	A*	`conf`	When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently? ICLR Ziang Song, Song Mei, Yu Bai
2021	A*	`conf`	A Sharp Analysis of Model-based Reinforcement Learning with Self-Play. ICML Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin
2021	A*	`conf`	Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification. ICML Yu Bai, Song Mei, Huan Wang, Caiming Xiong
2021	J	`jnl`	Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification. CoRR Yu Bai, Song Mei, Huan Wang, Caiming Xiong
2021	A*	`conf`	Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models. ICML Zitong Yang, Yu Bai, Song Mei
2021	J	`jnl`	Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models. CoRR Zitong Yang, Yu Bai, Song Mei
2021	A*	`conf`	How Important is the Train-Validation Split in Meta-Learning? ICML Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong
2021	J	`jnl`	Localized Calibration: Metrics and Recalibration. CoRR Rachel Luo, Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Silvio Savarese, Yu Bai, Shengjia Zhao, Stefano Ermon
2021	A*	`conf`	Near-Optimal Offline Reinforcement Learning via Double Variance Reduction. NeurIPS Ming Yin, Yu Bai, Yu-Xiang Wang
2021	J	`jnl`	Near-Optimal Offline Reinforcement Learning via Double Variance Reduction. CoRR Ming Yin, Yu Bai, Yu-Xiang Wang
2021	A	`conf`	Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning. AISTATS Ming Yin, Yu Bai, Yu-Xiang Wang
2021	A*	`conf`	Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning. NeurIPS Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, Yu Bai
2021	J	`jnl`	Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning. CoRR Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, Yu Bai
2021	A*	`conf`	Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games. NeurIPS Yu Bai, Chi Jin, Huan Wang, Caiming Xiong
2021	J	`jnl`	Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games. CoRR Yu Bai, Chi Jin, Huan Wang, Caiming Xiong
2021	A*	`conf`	Understanding the Under-Coverage Bias in Uncertainty Estimation. NeurIPS Yu Bai, Song Mei, Huan Wang, Caiming Xiong
2021	J	`jnl`	Understanding the Under-Coverage Bias in Uncertainty Estimation. CoRR Yu Bai, Song Mei, Huan Wang, Caiming Xiong
2021	J	`jnl`	When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently? CoRR Ziang Song, Song Mei, Yu Bai
2020	J	`jnl`	A Sharp Analysis of Model-based Reinforcement Learning with Self-Play. CoRR Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin
2020	A*	`conf`	Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks. ICLR Yu Bai, Jason D. Lee
2020	J	`jnl`	How Important is the Train-Validation Split in Meta-Learning? CoRR Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong
2020	J	`jnl`	Near Optimal Provable Uniform Convergence in Off-Policy Evaluation for Reinforcement Learning. CoRR Ming Yin, Yu Bai, Yu-Xiang Wang
2020	A*	`conf`	Near-Optimal Reinforcement Learning with Self-Play. NeurIPS Yu Bai, Chi Jin, Tiancheng Yu
2020	J	`jnl`	Near-Optimal Reinforcement Learning with Self-Play. CoRR Yu Bai, Chi Jin, Tiancheng Yu
2020	A*	`conf`	Provable Self-Play Algorithms for Competitive Reinforcement Learning. ICML Yu Bai, Chi Jin
2020	J	`jnl`	Provable Self-Play Algorithms for Competitive Reinforcement Learning. CoRR Yu Bai, Chi Jin
2020	J	`jnl`	Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width. CoRR Yu Bai, Ben Krause, Huan Wang, Caiming Xiong, Richard Socher
2020	A*	`conf`	Towards Understanding Hierarchical Learning: Benefits of Neural Representations. NeurIPS Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher
2020	J	`jnl`	Towards Understanding Hierarchical Learning: Benefits of Neural Representations. CoRR Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher
2019	—	`conf`	Approximability of Discriminators Implies Diversity in GANs. ICLR (Poster) Yu Bai, Tengyu Ma, Andrej Risteski
2019	J	`jnl`	Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks. CoRR Yu Bai, Jason D. Lee
2019	A*	`conf`	Provably Efficient Q-Learning with Low Switching Cost. NeurIPS Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang
2019	J	`jnl`	Provably Efficient Q-Learning with Low Switching Cost. CoRR Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang
2019	—	`conf`	ProxQuant: Quantized Neural Networks via Proximal Operators. ICLR (Poster) Yu Bai, Yu-Xiang Wang, Edo Liberty
2019	J	`jnl`	Proximal algorithms for constrained composite optimization, with applications to solving low-rank SDPs. CoRR Yu Bai, John C. Duchi, Song Mei
2019	—	`conf`	Subgradient Descent Learns Orthogonal Dictionaries. ICLR (Poster) Yu Bai, Qijia Jiang, Ju Sun
2018	J	`jnl`	Approximability of Discriminators Implies Diversity in GANs. CoRR Yu Bai, Tengyu Ma, Andrej Risteski
2018	J	`jnl`	ProxQuant: Quantized Neural Networks via Proximal Operators. CoRR Yu Bai, Yu-Xiang Wang, Edo Liberty
2018	J	`jnl`	Subgradient Descent Learns Orthogonal Dictionaries. CoRR Yu Bai, Qijia Jiang, Ju Sun