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ORCIDYu Bai 0017
Person information
- affiliation: Salesforce Research, Palo Alto, CA, USA
- affiliation (PhD 2019): Stanford University, CA, USA
Other persons with the same name
- Yu Bai — disambiguation page
- Yu Bai 0001 — Brown University, Division of Engineering, Providence, RI, USA
- Yu Bai 0002
![[0009-0002-1805-1503]](https://images.weserv.nl/?url=https%3A%2F%2Fdblp.uni-trier.de%2Fimg%2Forcid-mark.12x12.png&q=12&output=webp&max-age=110 "0009-0002-1805-1503")
— Shenyang Aerospace University, Research Center for Knowledge Engineering, China - Yu Bai 0003 — University of Kaiserslautern, Department of Computer Science, Embedded Systems Group, Germany
- Yu Bai 0004 — California State University, School of Engineering and Computer Science, Fullerton, CA, USA (and 1 more)
- Yu Bai 0005 — Chinese Academy of Sciences, Institute of Geographic Sciences and Natural Resources Research, Beijing, China
- Yu Bai 0006 — Xi'an Technological University, School of Mechatronic Engineering, China
- Yu Bai 0007 — Wuhan University, School of Electronic Information, China
- Yu Bai 0008 — Beijing University of Technology, Signal and Information Processing Laboratory, China
- Yu Bai 0009 — Beijing Institute of Technology, School of Optoelectronics, China
- Yu Bai 0010 — National University of Defense Technology, Science and Technology on Parallel and Distributed Laboratory, Changsha, China
- Yu Bai 0011 — Harbin Engineering University, College of Computer Science and Technology, China
- Yu Bai 0012 — Ocean University of China, School of Economics, Qingdao, China
- Yu Bai 0013 — Nanjing University of Aeronautics and Astronautics, College of Civil Aviation, China
- Yu Bai 0014 — Nanjing Audit University, Institute of Politics and Economics, China (and 1 more)
- Yu Bai 0015 — Southwest Petroleum University, School of Mechatronic Engineering, Chengdu, China
- Yu Bai 0016 — Beijing Normal University, Faculty of Geographical Science, China
- Yu Bai 0018 — Beijing Institute of Technology, School of Computer Science and Technology, China
- Yu Bai 0019 — University of New South Wales, Sydney, Australia
- Yu Bai 0020 — University of Posts and Telecommunications, State Key Laboratory of Networking and Switching Technology, China
- Yu Bai 0021 — Alibaba Group, Beijing, China
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2020 – today
- 2025
[c40]Shiyu Wang, Yihao Feng, Tian Lan, Ning Yu, Yu Bai, Ran Xu, Huan Wang, Caiming Xiong, Silvio Savarese:
Text2Data: Low-Resource Data Generation with Textual Control. AAAI 2025: 21252-21260- [i46]Seth Karten, Wenzhe Li, Zihan Ding, Samuel Kleiner, Yu Bai, Chi Jin:
LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra. CoRR abs/2507.15815 (2025) - 2024
- [j1]Minshuo Chen, Jie Meng, Yu Bai, Yinyu Ye, H. Vincent Poor, Mengdi Wang:
Efficient Reinforcement Learning With Impaired Observability: Learning to Act With Delayed and Missing State Observations. IEEE Trans. Inf. Theory 70(10): 7251-7272 (2024) - [c39]Tianyu Guo, Wei Hu, Song Mei, Huan Wang, Caiming Xiong, Silvio Savarese, Yu Bai:
How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations. ICLR 2024 - [c38]Jiacheng Guo, Minshuo Chen, Huan Wang, Caiming Xiong, Mengdi Wang, Yu Bai:
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight. ICLR 2024 - [c37]Licong Lin, Yu Bai, Song Mei:
Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining. ICLR 2024 - [c36]Lei Zhao, Mengdi Wang, Yu Bai:
Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective. ICML 2024 - [i45]Shiyu Wang, Yihao Feng, Tian Lan, Ning Yu, Yu Bai, Ran Xu, Huan Wang, Caiming Xiong, Silvio Savarese:
Text2Data: Low-Resource Data Generation with Textual Control. CoRR abs/2402.10941 (2024) - [i44]Ruiqi Zhang, Licong Lin, Yu Bai, Song Mei:
Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning. CoRR abs/2404.05868 (2024) - [i43]Tianyu Guo, Druv Pai, Yu Bai, Jiantao Jiao, Michael I. Jordan, Song Mei:
Active-Dormant Attention Heads: Mechanistically Demystifying Extreme-Token Phenomena in LLMs. CoRR abs/2410.13835 (2024) - 2023
- [c35]Yuanhao Wang, Qinghua Liu, Yu Bai, Chi Jin:
Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation. COLT 2023: 2793-2848 - [c34]Yuanhao Wang, Dingwen Kong, Yu Bai, Chi Jin:
Learning Rationalizable Equilibria in Multiplayer Games. ICLR 2023 - [c33]Fan Chen, Yu Bai, Song Mei:
Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms. ICLR 2023 - [c32]Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade:
The Role of Coverage in Online Reinforcement Learning. ICLR 2023 - [c31]Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Yu Bai:
Improved Online Conformal Prediction via Strongly Adaptive Online Learning. ICML 2023: 2337-2363 - [c30]Fan Chen, Huan Wang, Caiming Xiong, Song Mei, Yu Bai:
Lower Bounds for Learning in Revealing POMDPs. ICML 2023: 5104-5161 - [c29]Yu Bai, Fan Chen, Huan Wang, Caiming Xiong, Song Mei:
Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection. NeurIPS 2023 - [c28]Minshuo Chen, Yu Bai, H. Vincent Poor, Mengdi Wang:
Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations. NeurIPS 2023 - [c27]Hengyu Fu, Tianyu Guo, Yu Bai, Song Mei:
What can a Single Attention Layer Learn? A Study Through the Random Features Lens. NeurIPS 2023 - [i42]Fan Chen, Huan Wang, Caiming Xiong, Song Mei, Yu Bai:
Lower Bounds for Learning in Revealing POMDPs. CoRR abs/2302.01333 (2023) - [i41]Yuanhao Wang, Qinghua Liu, Yu Bai, Chi Jin:
Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation. CoRR abs/2302.06606 (2023) - [i40]Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Yu Bai:
Improved Online Conformal Prediction via Strongly Adaptive Online Learning. CoRR abs/2302.07869 (2023) - [i39]Minshuo Chen, Yu Bai, H. Vincent Poor, Mengdi Wang:
Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations. CoRR abs/2306.01243 (2023) - [i38]Yu Bai, Fan Chen, Huan Wang, Caiming Xiong, Song Mei:
Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection. CoRR abs/2306.04637 (2023) - [i37]Jiacheng Guo, Minshuo Chen, Huan Wang, Caiming Xiong, Mengdi Wang, Yu Bai:
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight. CoRR abs/2307.02884 (2023) - [i36]Hengyu Fu, Tianyu Guo, Yu Bai, Song Mei:
What can a Single Attention Layer Learn? A Study Through the Random Features Lens. CoRR abs/2307.11353 (2023) - [i35]Licong Lin, Yu Bai, Song Mei:
Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining. CoRR abs/2310.08566 (2023) - [i34]Tianyu Guo, Wei Hu, Song Mei, Huan Wang, Caiming Xiong, Silvio Savarese, Yu Bai:
How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations. CoRR abs/2310.10616 (2023) - [i33]Lei Zhao, Mengdi Wang, Yu Bai:
Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? CoRR abs/2312.00054 (2023) - 2022
- [c26]Prafulla Kumar Choubey, Yu Bai, Chien-Sheng Wu, Wenhao Liu, Nazneen Rajani:
Conformal Predictor for Improving Zero-Shot Text Classification Efficiency. EMNLP 2022: 3027-3034 - [c25]Yu Bai, Song Mei, Huan Wang, Yingbo Zhou, Caiming Xiong:
Efficient and Differentiable Conformal Prediction with General Function Classes. ICLR 2022 - [c24]Ziang Song, Song Mei, Yu Bai:
When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently? ICLR 2022 - [c23]Yu Bai, Chi Jin, Song Mei, Tiancheng Yu:
Near-Optimal Learning of Extensive-Form Games with Imperfect Information. ICML 2022: 1337-1382 - [c22]Yu Bai, Chi Jin, Song Mei, Ziang Song, Tiancheng Yu:
Efficient Phi-Regret Minimization in Extensive-Form Games via Online Mirror Descent. NeurIPS 2022 - [c21]Eshaan Nichani, Yu Bai, Jason D. Lee:
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials. NeurIPS 2022 - [c20]Ziang Song, Song Mei, Yu Bai:
Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games. NeurIPS 2022 - [c19]Runyu Zhang, Qinghua Liu, Huan Wang, Caiming Xiong, Na Li, Yu Bai:
Policy Optimization for Markov Games: Unified Framework and Faster Convergence. NeurIPS 2022 - [c18]Rachel Luo, Aadyot Bhatnagar, Yu Bai, Shengjia Zhao, Huan Wang, Caiming Xiong, Silvio Savarese, Stefano Ermon, Edward Schmerling, Marco Pavone:
Local calibration: metrics and recalibration. UAI 2022: 1286-1295 - [i32]Yu Bai, Chi Jin, Song Mei, Tiancheng Yu:
Near-Optimal Learning of Extensive-Form Games with Imperfect Information. CoRR abs/2202.01752 (2022) - [i31]Yu Bai, Song Mei, Huan Wang, Yingbo Zhou, Caiming Xiong:
Efficient and Differentiable Conformal Prediction with General Function Classes. CoRR abs/2202.11091 (2022) - [i30]Ziang Song, Song Mei, Yu Bai:
Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games. CoRR abs/2205.07223 (2022) - [i29]Yu Bai, Chi Jin, Song Mei, Ziang Song, Tiancheng Yu:
Efficient Φ-Regret Minimization in Extensive-Form Games via Online Mirror Descent. CoRR abs/2205.15294 (2022) - [i28]Runyu Zhang, Qinghua Liu, Huan Wang, Caiming Xiong, Na Li, Yu Bai:
Policy Optimization for Markov Games: Unified Framework and Faster Convergence. CoRR abs/2206.02640 (2022) - [i27]Eshaan Nichani, Yu Bai, Jason D. Lee:
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials. CoRR abs/2206.03688 (2022) - [i26]Fan Chen, Song Mei, Yu Bai:
Unified Algorithms for RL with Decision-Estimation Coefficients: No-Regret, PAC, and Reward-Free Learning. CoRR abs/2209.11745 (2022) - [i25]Fan Chen, Yu Bai, Song Mei:
Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms. CoRR abs/2209.14990 (2022) - [i24]Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade:
The Role of Coverage in Online Reinforcement Learning. CoRR abs/2210.04157 (2022) - [i23]Yuanhao Wang, Dingwen Kong, Yu Bai, Chi Jin:
Learning Rationalizable Equilibria in Multiplayer Games. CoRR abs/2210.11402 (2022) - [i22]Prafulla Kumar Choubey, Yu Bai, Chien-Sheng Wu, Wenhao Liu, Nazneen Rajani:
Conformal Predictor for Improving Zero-shot Text Classification Efficiency. CoRR abs/2210.12619 (2022) - 2021
- [c17]Ming Yin, Yu Bai, Yu-Xiang Wang:
Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning. AISTATS 2021: 1567-1575 - [c16]Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong:
How Important is the Train-Validation Split in Meta-Learning? ICML 2021: 543-553 - [c15]Yu Bai, Song Mei, Huan Wang, Caiming Xiong:
Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification. ICML 2021: 566-576 - [c14]Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin:
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play. ICML 2021: 7001-7010 - [c13]Zitong Yang, Yu Bai, Song Mei:
Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models. ICML 2021: 11704-11715 - [c12]Ming Yin, Yu Bai, Yu-Xiang Wang:
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction. NeurIPS 2021: 7677-7688 - [c11]Yu Bai, Song Mei, Huan Wang, Caiming Xiong:
Understanding the Under-Coverage Bias in Uncertainty Estimation. NeurIPS 2021: 18307-18319 - [c10]Yu Bai, Chi Jin, Huan Wang, Caiming Xiong:
Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games. NeurIPS 2021: 25799-25811 - [c9]Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, Yu Bai:
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning. NeurIPS 2021: 27395-27407 - [i21]Ming Yin, Yu Bai, Yu-Xiang Wang:
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction. CoRR abs/2102.01748 (2021) - [i20]Yu Bai, Song Mei, Huan Wang, Caiming Xiong:
Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification. CoRR abs/2102.07856 (2021) - [i19]Rachel Luo, Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Silvio Savarese, Yu Bai, Shengjia Zhao, Stefano Ermon:
Localized Calibration: Metrics and Recalibration. CoRR abs/2102.10809 (2021) - [i18]Yu Bai, Chi Jin, Huan Wang, Caiming Xiong:
Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games. CoRR abs/2102.11494 (2021) - [i17]Zitong Yang, Yu Bai, Song Mei:
Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models. CoRR abs/2103.04554 (2021) - [i16]Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, Yu Bai:
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning. CoRR abs/2106.04895 (2021) - [i15]Yu Bai, Song Mei, Huan Wang, Caiming Xiong:
Understanding the Under-Coverage Bias in Uncertainty Estimation. CoRR abs/2106.05515 (2021) - [i14]Ziang Song, Song Mei, Yu Bai:
When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently? CoRR abs/2110.04184 (2021) - 2020
- [c8]Yu Bai, Jason D. Lee:
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks. ICLR 2020 - [c7]Yu Bai, Chi Jin:
Provable Self-Play Algorithms for Competitive Reinforcement Learning. ICML 2020: 551-560 - [c6]Yu Bai, Chi Jin, Tiancheng Yu:
Near-Optimal Reinforcement Learning with Self-Play. NeurIPS 2020 - [c5]Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher:
Towards Understanding Hierarchical Learning: Benefits of Neural Representations. NeurIPS 2020 - [i13]Yu Bai, Ben Krause, Huan Wang, Caiming Xiong, Richard Socher:
Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width. CoRR abs/2002.04010 (2020) - [i12]Yu Bai, Chi Jin:
Provable Self-Play Algorithms for Competitive Reinforcement Learning. CoRR abs/2002.04017 (2020) - [i11]Yu Bai, Chi Jin, Tiancheng Yu:
Near-Optimal Reinforcement Learning with Self-Play. CoRR abs/2006.12007 (2020) - [i10]Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher:
Towards Understanding Hierarchical Learning: Benefits of Neural Representations. CoRR abs/2006.13436 (2020) - [i9]Ming Yin, Yu Bai, Yu-Xiang Wang:
Near Optimal Provable Uniform Convergence in Off-Policy Evaluation for Reinforcement Learning. CoRR abs/2007.03760 (2020) - [i8]Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin:
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play. CoRR abs/2010.01604 (2020) - [i7]Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong:
How Important is the Train-Validation Split in Meta-Learning? CoRR abs/2010.05843 (2020)
2010 – 2019
- 2019
- [c4]Yu Bai, Qijia Jiang, Ju Sun:
Subgradient Descent Learns Orthogonal Dictionaries. ICLR (Poster) 2019 - [c3]Yu Bai, Tengyu Ma, Andrej Risteski:
Approximability of Discriminators Implies Diversity in GANs. ICLR (Poster) 2019 - [c2]Yu Bai, Yu-Xiang Wang, Edo Liberty:
ProxQuant: Quantized Neural Networks via Proximal Operators. ICLR (Poster) 2019 - [c1]Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang:
Provably Efficient Q-Learning with Low Switching Cost. NeurIPS 2019: 8002-8011 - [i6]Yu Bai, John C. Duchi, Song Mei:
Proximal algorithms for constrained composite optimization, with applications to solving low-rank SDPs. CoRR abs/1903.00184 (2019) - [i5]Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang:
Provably Efficient Q-Learning with Low Switching Cost. CoRR abs/1905.12849 (2019) - [i4]Yu Bai, Jason D. Lee:
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks. CoRR abs/1910.01619 (2019) - 2018
- [i3]Yu Bai, Tengyu Ma, Andrej Risteski:
Approximability of Discriminators Implies Diversity in GANs. CoRR abs/1806.10586 (2018) - [i2]Yu Bai, Yu-Xiang Wang, Edo Liberty:
ProxQuant: Quantized Neural Networks via Proximal Operators. CoRR abs/1810.00861 (2018) - [i1]Yu Bai, Qijia Jiang, Ju Sun:
Subgradient Descent Learns Orthogonal Dictionaries. CoRR abs/1810.10702 (2018)
Coauthor Index
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