Yuexiang Zhai

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Yi Ma (马毅)Director of School of Computing & Data Science, Hong Kong University; Professor of EECS, UC BerkeleyAdresse e-mail validée de hku.hk
Shengbang TongNYU CourantAdresse e-mail validée de berkeley.edu
Sergey LevineUC Berkeley, Physical IntelligenceAdresse e-mail validée de eecs.berkeley.edu
Saining XieAssistant Professor at the Courant Institute, New York UniversityAdresse e-mail validée de nyu.edu
Qing QuAssistant Professor, Dept. of EECS, University of MichiganAdresse e-mail validée de umich.edu
Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York UniversityAdresse e-mail validée de cs.nyu.edu
Zhuang LiuResearch Scientist, FAIR, MetaAdresse e-mail validée de berkeley.edu
Zhihui ZhuAssistant Professor, Ohio State UniversityAdresse e-mail validée de osu.edu
John WrightElectrical Engineering, Columbia UniversityAdresse e-mail validée de columbia.edu
Xiao Li (李虓)Ph.D. candidate at University of MichiganAdresse e-mail validée de umich.edu
Yong Jae LeeAssociate Professor of Computer Sciences, UW-MadisonAdresse e-mail validée de wisc.edu
Mu CaiFinal-year PhD Student, University of Wisconsin-MadisonAdresse e-mail validée de cs.wisc.edu
Mitsuhiko NakamotoUC BerkeleyAdresse e-mail validée de eecs.berkeley.edu
Zhengyuan ZhouDept of Technology, Operations and Statistics at NYU SternAdresse e-mail validée de stern.nyu.edu
Xiao LiThe Chinese University of Hong Kong, ShenzhenAdresse e-mail validée de cuhk.edu.cn
Yuqian ZhangAssistant Professor, Rutgers UniversityAdresse e-mail validée de rutgers.edu
Chelsea FinnStanford University, Physical IntelligenceAdresse e-mail validée de cs.stanford.edu
Anikait SinghStanford UniversityAdresse e-mail validée de stanford.edu
Aviral KumarCMU & Google DeepMindAdresse e-mail validée de andrew.cmu.edu
Li-Yi WeiAdobe ResearchAdresse e-mail validée de adobe.com

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Yuexiang Zhai

Autres nomsSimon Zhai

UC Berkeley | Google DeepMind

Adresse e-mail validée de berkeley.edu - Page d'accueil

Artificial Intelligence Reinforcement Learning Machine Learning


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Eyes wide shut? exploring the visual shortcomings of multimodal llms S Tong, Z Liu, Y Zhai, Y Ma, Y LeCun, S Xie Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024	246	2024
Investigating the catastrophic forgetting in multimodal large language model fine-tuning Y Zhai, S Tong, X Li, M Cai, Q Qu, YJ Lee, Y Ma Conference on Parsimony and Learning (CPAL), 2024	134*	2024
Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning M Nakamoto, S Zhai, A Singh, M Sobol Mark, Y Ma, C Finn, A Kumar, ... Advances in Neural Information Processing Systems 36, 62244-62269, 2023	120	2023
Learning to reconstruct 3d manhattan wireframes from a single image Y Zhou, H Qi, Y Zhai, Q Sun, Z Chen, LY Wei, Y Ma Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019	78	2019
Complete dictionary learning via l4-norm maximization over the orthogonal group Y Zhai, Z Yang, Z Liao, J Wright, Y Ma Journal of Machine Learning Research (JMLR) 21 (165), 1-68, 2020	74	2020
Unpacking reward shaping: Understanding the benefits of reward engineering on sample complexity A Gupta, A Pacchiano, Y Zhai, S Kakade, S Levine Advances in Neural Information Processing Systems (NIPS) 35, 15281-15295, 2022	69	2022
Fine-tuning large vision-language models as decision-making agents via reinforcement learning S Zhai, H Bai, Z Lin, J Pan, P Tong, Y Zhou, A Suhr, S Xie, Y LeCun, Y Ma, ... Advances in Neural Information Processing Systems 37, 110935-110971, 2025	40	2025
Geometric analysis of nonconvex optimization landscapes for overcomplete learning Q Qu, Y Zhai, X Li, Y Zhang, Z Zhu International Conference on Learning Representations (ICLR), 2020	32	2020
Computational benefits of intermediate rewards for goal-reaching policy learning Y Zhai, C Baek, Z Zhou, J Jiao, Y Ma Journal of Artificial Intelligence Research (JAIR) 73, 847-896, 2022	28	2022
Convolutional normalization: Improving deep convolutional network robustness and training S Liu, X Li, Y Zhai, C You, Z Zhu, C Fernandez-Granda, Q Qu Advances in neural information processing systems (NIPS) 34, 28919-28928, 2021	28	2021
Understanding l4-based dictionary learning: Interpretation, stability, and robustness Y Zhai, H Mehta, Z Zhou, Y Ma International conference on learning representations (ICLR), 2020	23	2020
Lmrl gym: Benchmarks for multi-turn reinforcement learning with language models M Abdulhai, I White, C Snell, C Sun, J Hong, Y Zhai, K Xu, S Levine arXiv preprint arXiv:2311.18232, 2023	21	2023
RLIF: Interactive Imitation Learning as Reinforcement Learning J Luo, P Dong, Y Zhai, Y Ma, S Levine International conference on learning representations (ICLR), 2023	15	2023
Understanding the complexity gains of single-task rl with a curriculum Q Li, Y Zhai, Y Ma, S Levine International Conference on Machine Learning (ICML), 20412-20451, 2023	15	2023
Analysis of the optimization landscapes for overcomplete representation learning Q Qu, Y Zhai, X Li, Y Zhang, Z Zhu arXiv preprint arXiv:1912.02427, 2019	15	2019
White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is? Y Yu, S Buchanan, D Pai, T Chu, Z Wu, S Tong, H Bai, Y Zhai, ... Journal of Machine Learning Research 25 (300), 1-128, 2024	11*	2024
Closed-loop transcription via convolutional sparse coding X Dai, K Chen, S Tong, J Zhang, X Gao, M Li, D Pai, Y Zhai, XI Yuan, ... Conference on Parsimony and Learning (CPAL), 2024	7	2024
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training T Chu, Y Zhai, J Yang, S Tong, S Xie, D Schuurmans, QV Le, S Levine, ... arXiv preprint arXiv:2501.17161, 2025	3	2025

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