Yingbin Liang

ORCID

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 216

Top Venues

CoRR

IEEE Trans. Inf. Theory

NeurIPS

ISIT

Publications

Junze Deng, Yuan Cheng, Shaofeng Zou, Yingbin Liang
Sample Complexity Characterization for Linear Contextual MDPs. CoRR (2024)
Hongru Yang, Ziyu Jiang, Ruizhe Zhang, Zhangyang Wang, Yingbin Liang
Sharper analysis of sparsely activated wide neural networks with trainable biases. CoRR (2023)
Ming Shi, Yingbin Liang, Ness B. Shroff
Theoretical Hardness and Tractability of POMDPs in RL with Partial Hindsight State Information. CoRR (2023)
Junjie Yang, Tianlong Chen, Mingkang Zhu, Fengxiang He, Dacheng Tao, Yingbin Liang, Zhangyang Wang
Learning to Generalize Provably in Learning to Optimize. AISTATS (2023)
Sen Lin, Peizhong Ju, Yingbin Liang, Ness B. Shroff
Theory on Forgetting and Generalization of Continual Learning. ICML (2023)
Ziwei Guan, Yi Zhou, Yingbin Liang
Online Nonconvex Optimization with Limited Instantaneous Oracle Feedback. COLT (2023)
Ming Shi, Yingbin Liang, Ness B. Shroff
A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints. CoRR (2023)
Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang
Non-stationary Reinforcement Learning under General Function Approximation. ICML (2023)
Peizhong Ju, Sen Lin, Mark S. Squillante, Yingbin Liang, Ness B. Shroff
Generalization Performance of Transfer Learning: Overparameterized and Underparameterized Regimes. CoRR (2023)
Daouda Sow, Sen Lin, Zhangyang Wang, Yingbin Liang
Doubly Robust Instance-Reweighted Adversarial Training. CoRR (2023)
Yu Huang, Yuan Cheng, Yingbin Liang
In-Context Convergence of Transformers. CoRR (2023)
Yuan Cheng, Ruiquan Huang, Yingbin Liang, Jing Yang
Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs. ICLR (2023)
Songtao Feng, Ming Yin, Yu-Xiang Wang, Jing Yang, Yingbin Liang
Model-Free Algorithm with Improved Sample Efficiency for Zero-Sum Markov Games. CoRR (2023)
Yuan Cheng, Jing Yang, Yingbin Liang
Provably Efficient Algorithm for Nonstationary Low-Rank MDPs. CoRR (2023)
Junjie Yang, Xuxi Chen, Tianlong Chen, Zhangyang Wang, Yingbin Liang
M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation. ICLR (2023)
Ming Shi, Yingbin Liang, Ness B. Shroff
A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints. ICML (2023)
Peizhong Ju, Yingbin Liang, Ness B. Shroff
Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning. CoRR (2023)
Ming Shi, Yingbin Liang, Ness B. Shroff
Near-Optimal Adversarial Reinforcement Learning with Switching Costs. CoRR (2023)
Ruiquan Huang, Jing Yang, Yingbin Liang
Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-Free RL. ICLR (2023)
Ruiquan Huang, Yuan Cheng, Jing Yang, Vincent Tan, Yingbin Liang
Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes. CoRR (2023)
Junjie Yang, Tianlong Chen, Mingkang Zhu, Fengxiang He, Dacheng Tao, Yingbin Liang, Zhangyang Wang
Learning to Generalize Provably in Learning to Optimize. CoRR (2023)
Sen Lin, Daouda Sow, Kaiyi Ji, Yingbin Liang, Ness B. Shroff
Non-Convex Bilevel Optimization with Time-Varying Objective Functions. CoRR (2023)
Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang
Non-stationary Reinforcement Learning under General Function Approximation. CoRR (2023)
Yuan Cheng, Ruiquan Huang, Jing Yang, Yingbin Liang
Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs. CoRR (2023)
Yuan Cheng, Jing Yang, Yingbin Liang
Provably Efficient Algorithm for Nonstationary Low-Rank MDPs. NeurIPS (2023)
Junjie Yang, Tianlong Chen, Xuxi Chen, Zhangyang Wang, Yingbin Liang
Rethinking PGD Attack: Is Sign Function Necessary? CoRR (2023)
Hongru Yang, Yingbin Liang, Xiaojie Guo, Lingfei Wu, Zhangyang Wang
Theoretical Characterization of How Neural Network Pruning Affects its Generalization. CoRR (2023)
Junjie Yang, Xuxi Chen, Tianlong Chen, Zhangyang Wang, Yingbin Liang
M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation. CoRR (2023)
Daouda Sow, Sen Lin, Yingbin Liang, Junshan Zhang
Algorithm Design for Online Meta-Learning with Task Boundary Detection. CoRR (2023)
Xuyang Chen, Jingliang Duan, Yingbin Liang, Lin Zhao
Global Convergence of Two-Timescale Actor-Critic for Solving Linear Quadratic Regulator. AAAI (2023)
Junjie Yang, Jinze Zhao, Peihao Wang, Zhangyang Wang, Yingbin Liang
Meta ControlNet: Enhancing Task Adaptation via Meta Learning. CoRR (2023)
Sen Lin, Peizhong Ju, Yingbin Liang, Ness B. Shroff
Theory on Forgetting and Generalization of Continual Learning. CoRR (2023)
Sen Lin, Daouda Sow, Kaiyi Ji, Yingbin Liang, Ness B. Shroff
Non-Convex Bilevel Optimization with Time-Varying Objective Functions. NeurIPS (2023)
Ziyi Chen, Yi Zhou, Yingbin Liang, Zhaosong Lu
Generalized-Smooth Nonconvex Optimization is As Efficient As Smooth Nonconvex Optimization. ICML (2023)
Peizhong Ju, Yingbin Liang, Ness B. Shroff
Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning. ICLR (2023)
Ming Shi, Yingbin Liang, Ness B. Shroff
Near-Optimal Adversarial Reinforcement Learning with Switching Costs. ICLR (2023)
Kaiyi Ji, Yingbin Liang
Lower Bounds and Accelerated Algorithms for Bilevel Optimization. J. Mach. Learn. Res. 24 (2023)
Ruiquan Huang, Yingbin Liang, Jing Yang
Provably Efficient UCB-type Algorithms For Learning Predictive State Representations. CoRR (2023)
Yu Huang, Yingbin Liang, Longbo Huang
Provable Generalization of Overparameterized Meta-learning Trained with SGD. NeurIPS (2022)
Kaiyi Ji, Junjie Yang, Yingbin Liang
Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning. J. Mach. Learn. Res. 23 (2022)
Daouda Sow, Kaiyi Ji, Yingbin Liang
On the Convergence Theory for Hessian-Free Bilevel Algorithms. NeurIPS (2022)
Huaqing Xiong, Tengyu Xu, Lin Zhao, Yingbin Liang, Wei Zhang
Deterministic policy gradient: Convergence analysis. UAI (2022)
Kaiyi Ji, Mingrui Liu, Yingbin Liang, Lei Ying
Will Bilevel Optimizers Benefit from Loops. NeurIPS (2022)
Bin Dai, Chong Li, Yingbin Liang, Zheng Ma, Shlomo Shamai
Self-Secure Capacity-Achieving Feedback Schemes of Gaussian Multiple-Access Wiretap Channels With Degraded Message Sets. IEEE Trans. Inf. Forensics Secur. 17 (2022)
Tengyu Xu, Zhuoran Yang, Zhaoran Wang, Yingbin Liang
A Unifying Framework of Off-Policy General Value Function Evaluation. NeurIPS (2022)
Daouda Sow, Kaiyi Ji, Ziwei Guan, Yingbin Liang
A Constrained Optimization Approach to Bilevel Optimization with Multiple Inner Minima. CoRR (2022)
Shaocong Ma, Ziyi Chen, Yi Zhou, Kaiyi Ji, Yingbin Liang
Data Sampling Affects the Complexity of Online SGD over Dependent Data. CoRR (2022)
Sen Lin, Jialin Wan, Tengyu Xu, Yingbin Liang, Junshan Zhang
Model-Based Offline Meta-Reinforcement Learning with Regularization. ICLR (2022)
Yuan Cheng, Songtao Feng, Jing Yang, Hong Zhang, Yingbin Liang
Provable Benefit of Multitask Representation Learning in Reinforcement Learning. CoRR (2022)
Yuan Cheng, Songtao Feng, Jing Yang, Hong Zhang, Yingbin Liang
Provable Benefit of Multitask Representation Learning in Reinforcement Learning. NeurIPS (2022)
Xuyang Chen, Jingliang Duan, Yingbin Liang, Lin Zhao
Global Convergence of Two-timescale Actor-Critic for Solving Linear Quadratic Regulator. CoRR (2022)
Sen Lin, Jialin Wan, Tengyu Xu, Yingbin Liang, Junshan Zhang
Model-Based Offline Meta-Reinforcement Learning with Regularization. CoRR (2022)
Yi Zhou, Yingbin Liang, Huishuai Zhang
Understanding generalization error of SGD in nonconvex optimization. Mach. Learn. 111 (1) (2022)
Ziwei Guan, Tengyu Xu, Yingbin Liang
PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method. ICLR (2022)
Shaocong Ma, Ziyi Chen, Yi Zhou, Kaiyi Ji, Yingbin Liang
Data sampling affects the complexity of online SGD over dependent data. UAI (2022)
Tengyu Xu, Yingbin Liang
Provably Efficient Offline Reinforcement Learning with Trajectory-Wise Reward. CoRR (2022)
Yu Huang, Yingbin Liang, Longbo Huang
Provable Generalization of Overparameterized Meta-learning Trained with SGD. CoRR (2022)
Sen Lin, Ming Shi, Anish Arora, Raef Bassily, Elisa Bertino, Constantine Caramanis, Kaushik R. Chowdhury, Eylem Ekici, Atilla Eryilmaz, Stratis Ioannidis, Nan Jiang, Gauri Joshi, Jim Kurose, Yingbin Liang, Zhiqiang Lin, Jia Liu, Mingyan Liu, Tommaso Melodia, Aryan Mokhtari, Rob Nowak, Sewoong Oh, Srini Parthasarathy, Chunyi Peng, Hulya Seferoglu, Ness Shroff, Sanjay Shakkottai, Kannan Srinivasan, Ameet Talwalkar, Aylin Yener, Lei Ying
Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks. CIC (2022)
Ruiquan Huang, Jing Yang, Yingbin Liang
Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-free RL. CoRR (2022)
Kaiyi Ji, Mingrui Liu, Yingbin Liang, Lei Ying
Will Bilevel Optimizers Benefit from Loops. CoRR (2022)
Ziyi Chen, Yi Zhou, Tengyu Xu, Yingbin Liang
Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry. CoRR (2021)
Ziwei Guan, Tengyu Xu, Yingbin Liang
PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method. CoRR (2021)
Tengyu Xu, Yingbin Liang, Guanghui Lan
CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee. ICML (2021)
Ziwei Guan, Tengyu Xu, Yingbin Liang
When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence. AISTATS (2021)
Kaiyi Ji, Junjie Yang, Yingbin Liang
Bilevel Optimization: Convergence Analysis and Enhanced Design. ICML (2021)
Junjie Yang, Kaiyi Ji, Yingbin Liang
Provably Faster Algorithms for Bilevel Optimization. CoRR (2021)
Ziyi Chen, Yi Zhou, Tengyu Xu, Yingbin Liang
Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry. ICLR (2021)
Qunwei Li, Bhavya Kailkhura, Rushil Anirudh, Jize Zhang, Yi Zhou, Yingbin Liang, Thomas Yong-Jin Han, Pramod K. Varshney
MR-GAN: Manifold Regularized Generative Adversarial Networks for Scientific Data. SIAM J. Math. Data Sci. 3 (4) (2021)
Bowen Weng, Huaqing Xiong, Lin Zhao, Yingbin Liang, Wei Zhang
Finite-time theory for momentum Q-learning. UAI (2021)
Tengyu Xu, Zhuoran Yang, Zhaoran Wang, Yingbin Liang
A Unified Off-Policy Evaluation Approach for General Value Function. CoRR (2021)
Kaiyi Ji, Yingbin Liang
Lower Bounds and Accelerated Algorithms for Bilevel Optimization. CoRR (2021)
Daouda Sow, Kaiyi Ji, Yingbin Liang
ES-Based Jacobian Enables Faster Bilevel Optimization. CoRR (2021)
Junjie Yang, Kaiyi Ji, Yingbin Liang
Provably Faster Algorithms for Bilevel Optimization. NeurIPS (2021)
Huaqing Xiong, Tengyu Xu, Yingbin Liang, Wei Zhang
Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling. AAAI (2021)
Tianjiao Li, Ziwei Guan, Shaofeng Zou, Tengyu Xu, Yingbin Liang, Guanghui Lan
Faster Algorithm and Sharper Analysis for Constrained Markov Decision Process. CoRR (2021)
Tengyu Xu, Yingbin Liang
Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms. AISTATS (2021)
Lin Zhao, Huaqing Xiong, Yingbin Liang
Faster Non-asymptotic Convergence for Double Q-learning. NeurIPS (2021)
Kaiyi Ji, Yi Zhou, Yingbin Liang
Understanding Estimation and Generalization Error of Generative Adversarial Networks. IEEE Trans. Inf. Theory 67 (5) (2021)
Tengyu Xu, Zhuoran Yang, Zhaoran Wang, Yingbin Liang
Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality. CoRR (2021)
Tengyu Xu, Zhuoran Yang, Zhaoran Wang, Yingbin Liang
Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality. ICML (2021)
Tengyu Xu, Zhe Wang, Yingbin Liang
Improving Sample Complexity Bounds for Actor-Critic Algorithms. CoRR (2020)
Zhe Wang, Yingbin Liang, Pengsheng Ji
Spectral Algorithms for Community Detection in Directed Networks. J. Mach. Learn. Res. 21 (2020)
Kaiyi Ji, Junjie Yang, Yingbin Liang
Provably Faster Algorithms for Bilevel Optimization and Applications to Meta-Learning. CoRR (2020)
Tengyu Xu, Zhe Wang, Yingbin Liang, H. Vincent Poor
Enhanced First and Zeroth Order Variance Reduced Algorithms for Min-Max Optimization. CoRR (2020)
Tengyu Xu, Zhe Wang, Yi Zhou, Yingbin Liang
Reanalysis of Variance Reduced Temporal Difference Learning. ICLR (2020)
Tengyu Xu, Zhe Wang, Yi Zhou, Yingbin Liang
Reanalysis of Variance Reduced Temporal Difference Learning. CoRR (2020)
Bin Dai, Chong Li, Yingbin Liang, Zheng Ma, Shlomo Shamai Shitz
Feedback Capacity of Gaussian Multiple-Access Wiretap Channel with Degraded Message Sets. ITW (2020)
Kaiyi Ji, Jason D. Lee, Yingbin Liang, H. Vincent Poor
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters. CoRR (2020)
Bin Dai, Chong Li, Yingbin Liang, Zheng Ma, Shlomo Shamai Shitz
Impact of Action-Dependent State and Channel Feedback on Gaussian Wiretap Channels. IEEE Trans. Inf. Theory 66 (6) (2020)
Kaiyi Ji, Junjie Yang, Yingbin Liang
Multi-Step Model-Agnostic Meta-Learning: Convergence and Improved Algorithms. CoRR (2020)
Bowen Weng, Huaqing Xiong, Yingbin Liang, Wei Zhang
Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descent. CoRR (2020)
Tengyu Xu, Zhe Wang, Yingbin Liang
Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms. NeurIPS (2020)
Bin Dai, Chong Li, Yingbin Liang, Zheng Ma, Shlomo Shamai
Feedback Capacities of Gaussian Multiple-Access Wiretap Channels. CoRR (2020)
Tengyu Xu, Zhe Wang, Yingbin Liang
Non-asymptotic Convergence Analysis of Two Time-scale (Natural) Actor-Critic Algorithms. CoRR (2020)
Kaiyi Ji, Zhe Wang, Bowen Weng, Yi Zhou, Wei Zhang, Yingbin Liang
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms. ICML (2020)
Huaqing Xiong, Lin Zhao, Yingbin Liang, Wei Zhang
Finite-Time Analysis for Double Q-learning. NeurIPS (2020)
Yi Zhou, Zhe Wang, Kaiyi Ji, Yingbin Liang, Vahid Tarokh
Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization. IJCAI (2020)
Ziwei Guan, Kaiyi Ji, Donald J. Bucci Jr., Timothy Y. Hu, Joseph Palombo, Michael Liston, Yingbin Liang
Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack. CoRR (2020)
Ziwei Guan, Tengyu Xu, Yingbin Liang
When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence. CoRR (2020)
Tengyu Xu, Yingbin Liang, Guanghui Lan
A Primal Approach to Constrained Policy Optimization: Global Optimality and Finite-Time Analysis. CoRR (2020)

Yingbin Liang.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Yingbin Liang