Liang, Yingbin

96 publications

ICLR 2025 A Theoretical Analysis of Self-Supervised Learning for Vision Transformers Yu Huang, Zixin Wen, Yuejie Chi, Yingbin Liang
NeurIPS 2025 Absorb and Converge: Provable Convergence Guarantee for Absorbing Discrete Diffusion Models Yuchen Liang, Renxiang Huang, Lifeng Lai, Ness Shroff, Yingbin Liang
CPAL 2025 AdaProx: A Novel Method for Bilevel Optimization Under Pessimistic Framework Ziwei Guan, Daouda Sow, Sen Lin, Yingbin Liang
ICLR 2025 Broadening Target Distributions for Accelerated Diffusion Models via a Novel Analysis Approach Yuchen Liang, Peizhong Ju, Yingbin Liang, Ness Shroff
ICLR 2025 DUET: Decentralized Bilevel Optimization Without Lower-Level Strong Convexity Zhen Qin, Zhuqing Liu, Songtao Lu, Yingbin Liang, Jia Liu
NeurIPS 2025 Discrete Diffusion Models: Novel Analysis and New Sampler Guarantees Yuchen Liang, Yingbin Liang, Lifeng Lai, Ness Shroff
ICLR 2025 Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Daouda Sow, Herbert Woisetschläger, Saikiran Bulusu, Shiqiang Wang, Hans Arno Jacobsen, Yingbin Liang
ICML 2025 How Transformers Learn Regular Language Recognition: A Theoretical Study on Training Dynamics and Implicit Bias Ruiquan Huang, Yingbin Liang, Jing Yang
CPAL 2025 Meta ControlNet: Enhancing Task Adaptation via Meta Learning Junjie Yang, Jinze Zhao, Peihao Wang, Zhangyang Wang, Yingbin Liang
NeurIPS 2025 Multi-Head Transformers Provably Learn Symbolic Multi-Step Reasoning via Gradient Descent Tong Yang, Yu Huang, Yingbin Liang, Yuejie Chi
JMLR 2025 Random Pruning Over-Parameterized Neural Networks Can Improve Generalization: A Training Dynamics Analysis Hongru Yang, Yingbin Liang, Xiaojie Guo, Lingfei Wu, Zhangyang Wang
ICLR 2025 Theory on Mixture-of-Experts in Continual Learning Hongbo Li, Sen Lin, Lingjie Duan, Yingbin Liang, Ness Shroff
ICLR 2025 Theory on Score-Mismatched Diffusion Models and Zero-Shot Conditional Samplers Yuchen Liang, Peizhong Ju, Yingbin Liang, Ness Shroff
ICLR 2025 Transformers Provably Learn Two-Mixture of Linear Classification via Gradient Flow Hongru Yang, Zhangyang Wang, Jason D. Lee, Yingbin Liang
ICML 2025 Unlocking the Power of Rehearsal in Continual Learning: A Theoretical Perspective Junze Deng, Qinhang Wu, Peizhong Ju, Sen Lin, Yingbin Liang, Ness Shroff
CPAL 2024 Algorithm Design for Online Meta-Learning with Task Boundary Detection Daouda Sow, Sen Lin, Yingbin Liang, Junshan Zhang
ICLR 2024 Doubly Robust Instance-Reweighted Adversarial Training Daouda Sow, Sen Lin, Zhangyang Wang, Yingbin Liang
NeurIPSW 2024 Enhancing Generalization in Sparse Mixture of Experts Models: The Case for Increased Expert Activation in Compositional Tasks Jinze Zhao, Junjie Yang, Peihao Wang, Yingbin Liang, Zhangyang Wang
ICMLW 2024 How Transformers Learn Diverse Attention Correlations in Masked Vision Pretraining Yu Huang, Zixin Wen, Yuejie Chi, Yingbin Liang
ICML 2024 Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games Songtao Feng, Ming Yin, Yu-Xiang Wang, Jing Yang, Yingbin Liang
ICML 2024 In-Context Convergence of Transformers Yu Huang, Yuan Cheng, Yingbin Liang
NeurIPS 2024 In-Context Learning with Representations: Contextual Generalization of Trained Transformers Tong Yang, Yu Huang, Yingbin Liang, Yuejie Chi
ICMLW 2024 In-Context Learning with Representations: Contextual Generalization of Trained Transformers Tong Yang, Yu Huang, Yingbin Liang, Yuejie Chi
NeurIPSW 2024 Model Developmental Safety: A Safety-Centric Method and Applications in Vision-Language Models Gang Li, Wendi Yu, Yao Yao, Wei Tong, Yingbin Liang, Qihang Lin, Tianbao Yang
JMLR 2024 Neural Networks with Sparse Activation Induced by Large Bias: Tighter Analysis with Bias-Generalized NTK Hongru Yang, Ziyu Jiang, Ruizhe Zhang, Yingbin Liang, Zhangyang Wang
NeurIPS 2024 Non-Asymptotic Convergence of Training Transformers for Next-Token Prediction Ruiquan Huang, Yingbin Liang, Jing Yang
ICLR 2024 On the Hardness of Online Nonconvex Optimization with Single Oracle Feedback Ziwei Guan, Yi Zhou, Yingbin Liang
ICLR 2024 Provable Benefits of Multi-Task RL Under Non-Markovian Decision Making Processes Ruiquan Huang, Yuan Cheng, Jing Yang, Vincent Tan, Yingbin Liang
ICLR 2024 Provably Efficient UCB-Type Algorithms for Learning Predictive State Representations Ruiquan Huang, Yingbin Liang, Jing Yang
AISTATS 2024 Sample Complexity Characterization for Linear Contextual MDPs Junze Deng, Yuan Cheng, Shaofeng Zou, Yingbin Liang
NeurIPS 2024 Training Dynamics of Transformers to Recognize Word Co-Occurrence via Gradient Flow Analysis Hongru Yang, Bhavya Kailkhura, Zhangyang Wang, Yingbin Liang
ICML 2023 A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints Ming Shi, Yingbin Liang, Ness Shroff
ICML 2023 Generalized-Smooth Nonconvex Optimization Is as Efficient as Smooth Nonconvex Optimization Ziyi Chen, Yi Zhou, Yingbin Liang, Zhaosong Lu
AAAI 2023 Global Convergence of Two-Timescale Actor-Critic for Solving Linear Quadratic Regulator Xuyang Chen, Jingliang Duan, Yingbin Liang, Lin Zhao
ICLR 2023 Improved Sample Complexity for Reward-Free Reinforcement Learning Under Low-Rank MDPs Yuan Cheng, Ruiquan Huang, Yingbin Liang, Jing Yang
NeurIPSW 2023 In-Context Convergence of Transformers Yu Huang, Yuan Cheng, Yingbin Liang
AISTATS 2023 Learning to Generalize Provably in Learning to Optimize Junjie Yang, Tianlong Chen, Mingkang Zhu, Fengxiang He, Dacheng Tao, Yingbin Liang, Zhangyang Wang
JMLR 2023 Lower Bounds and Accelerated Algorithms for Bilevel Optimization Kaiyi Ji, Yingbin Liang
ICLR 2023 M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation Junjie Yang, Xuxi Chen, Tianlong Chen, Zhangyang Wang, Yingbin Liang
ICLR 2023 Near-Optimal Adversarial Reinforcement Learning with Switching Costs Ming Shi, Yingbin Liang, Ness Shroff
NeurIPS 2023 Non-Convex Bilevel Optimization with Time-Varying Objective Functions Sen Lin, Daouda Sow, Kaiyi Ji, Yingbin Liang, Ness Shroff
ICML 2023 Non-Stationary Reinforcement Learning Under General Function Approximation Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang
TMLR 2023 Online Min-Max Problems with Non-Convexity and Non-Stationarity Yu Huang, Yuan Cheng, Yingbin Liang, Longbo Huang
COLT 2023 Online Nonconvex Optimization with Limited Instantaneous Oracle Feedback Ziwei Guan, Yi Zhou, Yingbin Liang
NeurIPS 2023 Provably Efficient Algorithm for Nonstationary Low-Rank MDPs Yuan Cheng, Jing Yang, Yingbin Liang
ICLR 2023 Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-Free RL Ruiquan Huang, Jing Yang, Yingbin Liang
ICLR 2023 Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning Peizhong Ju, Yingbin Liang, Ness Shroff
ICML 2023 Theory on Forgetting and Generalization of Continual Learning Sen Lin, Peizhong Ju, Yingbin Liang, Ness Shroff
NeurIPS 2022 A Unifying Framework of Off-Policy General Value Function Evaluation Tengyu Xu, Zhuoran Yang, Zhaoran Wang, Yingbin Liang
UAI 2022 Data Sampling Affects the Complexity of Online SGD over Dependent Data Shaocong Ma, Ziyi Chen, Yi Zhou, Kaiyi Ji, Yingbin Liang
UAI 2022 Deterministic Policy Gradient: Convergence Analysis Huaqing. Xiong, Tengyu Xu, Lin Zhao, Yingbin Liang, Wei Zhang
ICLR 2022 Model-Based Offline Meta-Reinforcement Learning with Regularization Sen Lin, Jialin Wan, Tengyu Xu, Yingbin Liang, Junshan Zhang
NeurIPS 2022 On the Convergence Theory for Hessian-Free Bilevel Algorithms Daouda Sow, Kaiyi Ji, Yingbin Liang
NeurIPSW 2022 Online Min-Max Optimization: Nonconvexity, Nonstationarity, and Dynamic Regret Yu Huang, Yuan Cheng, Yingbin Liang, Longbo Huang
ICLR 2022 PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method Ziwei Guan, Tengyu Xu, Yingbin Liang
NeurIPS 2022 Provable Benefit of Multitask Representation Learning in Reinforcement Learning Yuan Cheng, Songtao Feng, Jing Yang, Hong Zhang, Yingbin Liang
NeurIPS 2022 Provable Generalization of Overparameterized Meta-Learning Trained with SGD Yu Huang, Yingbin Liang, Longbo Huang
JMLR 2022 Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning Kaiyi Ji, Junjie Yang, Yingbin Liang
MLJ 2022 Understanding Generalization Error of SGD in Nonconvex Optimization Yi Zhou, Yingbin Liang, Huishuai Zhang
NeurIPS 2022 Will Bilevel Optimizers Benefit from Loops Kaiyi Ji, Mingrui Liu, Yingbin Liang, Lei Ying
AISTATS 2021 Sample Complexity Bounds for Two Timescale Value-Based Reinforcement Learning Algorithms Tengyu Xu, Yingbin Liang
AISTATS 2021 When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence Ziwei Guan, Tengyu Xu, Yingbin Liang
ICML 2021 Bilevel Optimization: Convergence Analysis and Enhanced Design Kaiyi Ji, Junjie Yang, Yingbin Liang
ICML 2021 CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee Tengyu Xu, Yingbin Liang, Guanghui Lan
ICML 2021 Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality Tengyu Xu, Zhuoran Yang, Zhaoran Wang, Yingbin Liang
NeurIPS 2021 Faster Non-Asymptotic Convergence for Double Q-Learning Lin Zhao, Huaqing Xiong, Yingbin Liang
AAAI 2021 Non-Asymptotic Convergence of Adam-Type Reinforcement Learning Algorithms Under Markovian Sampling Huaqing Xiong, Tengyu Xu, Yingbin Liang, Wei Zhang
NeurIPS 2021 Provably Faster Algorithms for Bilevel Optimization Junjie Yang, Kaiyi Ji, Yingbin Liang
ICLR 2021 Proximal Gradient Descent-Ascent: Variable Convergence Under KŁ Geometry Ziyi Chen, Yi Zhou, Tengyu Xu, Yingbin Liang
IJCAI 2020 Analysis of Q-Learning with Adaptation and Momentum Restart for Gradient Descent Bowen Weng, Huaqing Xiong, Yingbin Liang, Wei Zhang
NeurIPS 2020 Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters Kaiyi Ji, Jason Lee, Yingbin Liang, H. Vincent Poor
NeurIPS 2020 Finite-Time Analysis for Double Q-Learning Huaqing Xiong, Lin Zhao, Yingbin Liang, Wei Zhang
ICML 2020 History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms Kaiyi Ji, Zhe Wang, Bowen Weng, Yi Zhou, Wei Zhang, Yingbin Liang
NeurIPS 2020 Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms Tengyu Xu, Zhe Wang, Yingbin Liang
IJCAI 2020 Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization Yi Zhou, Zhe Wang, Kaiyi Ji, Yingbin Liang, Vahid Tarokh
ICLR 2020 Reanalysis of Variance Reduced Temporal Difference Learning Tengyu Xu, Zhe Wang, Yi Zhou, Yingbin Liang
AAAI 2020 Robust Stochastic Bandit Algorithms Under Probabilistic Unbounded Adversarial Attack Ziwei Guan, Kaiyi Ji, Donald J. Bucci Jr., Timothy Y. Hu, Joseph Palombo, Michael Liston, Yingbin Liang
JMLR 2020 Spectral Algorithms for Community Detection in Directed Networks Zhe Wang, Yingbin Liang, Pengsheng Ji
UAI 2019 Cubic Regularization with Momentum for Nonconvex Optimization Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan
NeurIPS 2019 Finite-Sample Analysis for SARSA with Linear Function Approximation Shaofeng Zou, Tengyu Xu, Yingbin Liang
ICML 2019 Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang
ICLR 2019 SGD Converges to Global Minimum in Deep Learning via Star-Convex Path Yi Zhou, Junjie Yang, Huishuai Zhang, Yingbin Liang, Vahid Tarokh
NeurIPS 2019 SpiderBoost and Momentum: Faster Variance Reduction Algorithms Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh
AISTATS 2019 Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan
NeurIPS 2019 Two Time-Scale Off-Policy TD Learning: Non-Asymptotic Analysis over Markovian Samples Tengyu Xu, Shaofeng Zou, Yingbin Liang
NeurIPS 2018 Convergence of Cubic Regularization for Nonconvex Optimization Under KL Property Yi Zhou, Zhe Wang, Yingbin Liang
ICLR 2018 Critical Points of Linear Neural Networks: Analytical Forms and Landscape Properties Yi Zhou, Yingbin Liang
JMLR 2018 Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters Yi Zhou, Yingbin Liang, Yaoliang Yu, Wei Dai, Eric P. Xing
NeurIPS 2018 Minimax Estimation of Neural Net Distance Kaiyi Ji, Yingbin Liang
JMLR 2017 A Nonconvex Approach for Phase Retrieval: Reshaped Wirtinger Flow and Incremental Algorithms Huishuai Zhang, Yingbin Liang, Yuejie Chi
ICML 2017 Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization Qunwei Li, Yi Zhou, Yingbin Liang, Pramod K. Varshney
AISTATS 2016 On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric P. Xing
ICML 2016 Provable Non-Convex Phase Retrieval with Outliers: Median TruncatedWirtinger Flow Huishuai Zhang, Yuejie Chi, Yingbin Liang
NeurIPS 2016 Reshaped Wirtinger Flow for Solving Quadratic System of Equations Huishuai Zhang, Yingbin Liang
NeurIPS 2015 Analysis of Robust PCA via Local Incoherence Huishuai Zhang, Yi Zhou, Yingbin Liang
AISTATS 2013 Block Regularized Lasso for Multivariate Multi-Response Linear Regression Weiguang Wang, Yingbin Liang, Eric P. Xing