Lin F. Yang

ORCID

Publication Activity (10 Years)

Years Active: 2014-2023
Publications (10 Years): 110

Top Venues

Publications

Jiayi Huang, Han Zhong, Liwei Wang, Lin F. Yang
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds. CoRR (2023)
Osama A. Hanna, Merve Karakas, Lin F. Yang, Christina Fragouli
Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels. CoRR (2023)
Dingwen Kong, Lin F. Yang
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning. CoRR (2023)
Sanae Amani, Khushbu Pahwa, Vladimir Braverman, Lin F. Yang
Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing. CoRR (2023)
Jialin Dong, Lin F. Yang
Does Sparsity Help in Learning Misspecified Linear Bandits? CoRR (2023)
Masoud Monajatipoor, Liunian Harold Li, Mozhdeh Rouhsedaghat, Lin F. Yang, Kai-Wei Chang
MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models. CoRR (2023)
Amin Karbasi, Grigoris Velegkas, Lin F. Yang, Felix Zhou
Replicability in Reinforcement Learning. CoRR (2023)
Haochen Zhang, Xi Chen, Lin F. Yang
Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning. CoRR (2023)
Osama A. Hanna, Merve Karakas, Lin F. Yang, Christina Fragouli
Multi-Arm Bandits over Action Erasure Channels. ISIT (2023)
Jiayi Huang, Han Zhong, Liwei Wang, Lin F. Yang
Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximation. CoRR (2023)
Kaiqing Zhang, Sham M. Kakade, Tamer Basar, Lin F. Yang
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity. J. Mach. Learn. Res. 24 (2023)
Sharan Vaswani, Lin F. Yang, Csaba Szepesvári
Near-Optimal Sample Complexity Bounds for Constrained MDPs. CoRR (2022)
Sanae Amani, Tor Lattimore, András György, Lin F. Yang
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. CoRR (2022)
Osama A. Hanna, Lin F. Yang, Christina Fragouli
Learning in Distributed Contextual Linear Bandits Without Sharing the Context. CoRR (2022)
Osama A. Hanna, Lin F. Yang, Christina Fragouli
Compression for Multi-Arm Bandits. IEEE J. Sel. Areas Inf. Theory 3 (4) (2022)
Osama A. Hanna, Lin F. Yang, Christina Fragouli
Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms. CoRR (2022)
Sanae Amani, Lin F. Yang, Ching-An Cheng
Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximation. CoRR (2022)
Jinghan Wang, Mengdi Wang, Lin F. Yang
Near Sample-Optimal Reduction-based Policy Learning for Average Reward MDP. CoRR (2022)
Sanae Amani, Lin F. Yang
Doubly Pessimistic Algorithms for Strictly Safe Off-Policy Optimization. CISS (2022)
Vladimir Braverman, Robert Krauthgamer, Lin F. Yang
Universal Streaming of Subset Norms. Theory Comput. 18 (2022)
Yuanzhi Li, Ruosong Wang, Lin F. Yang
Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning. FOCS (2022)
Sanae Amani, Christos Thrampoulidis, Lin F. Yang
Safe Reinforcement Learning with Linear Function Approximation. CoRR (2021)
Jialin Dong, Da Zheng, Lin F. Yang, George Karypis
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs. KDD (2021)
Yuanzhi Li, Ruosong Wang, Lin F. Yang
Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning. CoRR (2021)
Han Zhong, Jiayi Huang, Lin F. Yang, Liwei Wang
Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs. CoRR (2021)
Nived Rajaraman, Yanjun Han, Lin F. Yang, Kannan Ramchandran, Jiantao Jiao
Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally. CoRR (2021)
Fei Feng, Wotao Yin, Alekh Agarwal, Lin F. Yang
Provably Correct Optimization and Exploration with Non-linear Policies. CoRR (2021)
Junhong Shen, Lin F. Yang
Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation. AAAI (2021)
Jialin Dong, Da Zheng, Lin F. Yang, George Karypis
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs. CoRR (2021)
Haque Ishfaq, Qiwen Cui, Viet Nguyen, Alex Ayoub, Zhuoran Yang, Zhaoran Wang, Doina Precup, Lin F. Yang
Randomized Exploration for Reinforcement Learning with General Value Function Approximation. CoRR (2021)
Jingfeng Wu, Vladimir Braverman, Lin F. Yang
Gap-Dependent Unsupervised Exploration for Reinforcement Learning. CoRR (2021)
Dingwen Kong, Ruslan Salakhutdinov, Ruosong Wang, Lin F. Yang
Online Sub-Sampling for Reinforcement Learning with General Function Approximation. CoRR (2021)
Yuanzhi Li, Ruosong Wang, Lin F. Yang
Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning. FOCS (2021)
Osama A. Hanna, Lin F. Yang, Christina Fragouli
Solving Multi-Arm Bandit Using a Few Bits of Communication. CoRR (2021)
Minbo Gao, Tianle Xie, Simon S. Du, Lin F. Yang
A Provably Efficient Algorithm for Linear Markov Decision Process with Low Switching Cost. CoRR (2021)
Xiaoyu Chen, Jiachen Hu, Lin F. Yang, Liwei Wang
Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver. CoRR (2021)
Weichao Mao, Tamer Basar, Lin F. Yang, Kaiqing Zhang
Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration. CoRR (2021)
Junhong Shen, Lin F. Yang
Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation. CoRR (2021)
Kunhe Yang, Lin F. Yang, Simon S. Du
Q-learning with Logarithmic Regret. AISTATS (2021)
Qiwen Cui, Lin F. Yang
Minimax sample complexity for turn-based stochastic game. UAI (2021)
Fei Feng, Ruosong Wang, Wotao Yin, Simon S. Du, Lin F. Yang
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning. NeurIPS (2020)
Ruosong Wang, Russ R. Salakhutdinov, Lin F. Yang
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension. NeurIPS (2020)
Alex Ayoub, Zeyu Jia, Csaba Szepesvári, Mengdi Wang, Lin F. Yang
Model-Based Reinforcement Learning with Value-Targeted Regression. CoRR (2020)
Yingyu Liang, Zhao Song, Mengdi Wang, Lin F. Yang, Xin Yang
Sketching Transformed Matrices with Applications to Natural Language Processing. CoRR (2020)
Vladimir Braverman, Moses Charikar, William Kuszmaul, Lin F. Yang
The one-way communication complexity of dynamic time warping distance. J. Comput. Geom. 11 (2) (2020)
Kaiqing Zhang, Sham M. Kakade, Tamer Basar, Lin F. Yang
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity. CoRR (2020)
Yichong Xu, Ruosong Wang, Lin F. Yang, Aarti Singh, Artur Dubrawski
Preference-based Reinforcement Learning with Finite-Time Guarantees. NeurIPS (2020)
Ruosong Wang, Simon S. Du, Lin F. Yang, Russ R. Salakhutdinov
On Reward-Free Reinforcement Learning with Linear Function Approximation. NeurIPS (2020)
Tianyu Wang, Lin F. Yang
Episodic Linear Quadratic Regulators with Low-rank Transitions. CoRR (2020)
Yichong Xu, Ruosong Wang, Lin F. Yang, Aarti Singh, Artur Dubrawski
Preference-based Reinforcement Learning with Finite-Time Guarantees. CoRR (2020)
Ruosong Wang, Ruslan Salakhutdinov, Lin F. Yang
Provably Efficient Reinforcement Learning with General Value Function Approximation. CoRR (2020)
Jingfeng Wu, Vladimir Braverman, Lin F. Yang
Obtaining Adjustable Regularization for Free via Iterate Averaging. CoRR (2020)
Simon S. Du, Sham M. Kakade, Ruosong Wang, Lin F. Yang
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? ICLR (2020)
Qiwen Cui, Lin F. Yang
Minimax Sample Complexity for Turn-based Stochastic Game. CoRR (2020)
Gabriel I. Fernandez, Colin Togashi, Dennis W. Hong, Lin F. Yang
Deep Reinforcement Learning with Linear Quadratic Regulator Regions. CoRR (2020)
Ruosong Wang, Simon S. Du, Lin F. Yang, Sham M. Kakade
Is Long Horizon RL More Difficult Than Short Horizon RL? NeurIPS (2020)
Nived Rajaraman, Lin F. Yang, Jiantao Jiao, Kannan Ramchandran
Toward the Fundamental Limits of Imitation Learning. NeurIPS (2020)
Alekh Agarwal, Sham M. Kakade, Lin F. Yang
Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal. COLT (2020)
Ruosong Wang, Peilin Zhong, Simon S. Du, Russ R. Salakhutdinov, Lin F. Yang
Planning with General Objective Functions: Going Beyond Total Rewards. NeurIPS (2020)
Ruosong Wang, Simon S. Du, Lin F. Yang, Ruslan Salakhutdinov
On Reward-Free Reinforcement Learning with Linear Function Approximation. CoRR (2020)
Kaiqing Zhang, Sham M. Kakade, Tamer Basar, Lin F. Yang
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity. NeurIPS (2020)
Nived Rajaraman, Lin F. Yang, Jiantao Jiao, Kannan Ramchandran
Toward the Fundamental Limits of Imitation Learning. CoRR (2020)
Jingfeng Wu, Vladimir Braverman, Lin F. Yang
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning. CoRR (2020)
Qiwen Cui, Lin F. Yang
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning? CoRR (2020)
Fei Feng, Ruosong Wang, Wotao Yin, Simon S. Du, Lin F. Yang
Provably Efficient Exploration for RL with Unsupervised Learning. CoRR (2020)
Qiwen Cui, Lin F. Yang
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning? NeurIPS (2020)
Tianyu Wang, Lin F. Yang, Zizhuo Wang
Random Walk Bandits. CoRR (2020)
Ruosong Wang, Simon S. Du, Lin F. Yang, Sham M. Kakade
Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning? CoRR (2020)
Kunhe Yang, Lin F. Yang, Simon S. Du
Q-learning with Logarithmic Regret. CoRR (2020)
Simon S. Du, Ruosong Wang, Mengdi Wang, Lin F. Yang
Continuous Control with Contexts, Provably. CoRR (2019)
Vladimir Braverman, Moses Charikar, William Kuszmaul, David P. Woodruff, Lin F. Yang
The One-Way Communication Complexity of Dynamic Time Warping Distance. SoCG (2019)
Chengzhuo Ni, Lin F. Yang, Mengdi Wang
Learning to Control in Metric Space with Optimal Regret. Allerton (2019)
Zhao Song, Ruosong Wang, Lin F. Yang, Hongyang Zhang, Peilin Zhong
Efficient Symmetric Norm Regression via Linear Sketching. CoRR (2019)
Aaron Sidford, Mengdi Wang, Lin F. Yang, Yinyu Ye
Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity. CoRR (2019)
Alekh Agarwal, Sham M. Kakade, Lin F. Yang
On the Optimality of Sparse Model-Based Planning for Markov Decision Processes. CoRR (2019)
Zhuoran Yang, Lin F. Yang, Ethan X. Fang, Tuo Zhao, Zhaoran Wang, Matey Neykov
Misspecified nonconvex statistical optimization for sparse phase retrieval. Math. Program. 176 (1-2) (2019)
Zeyu Jia, Lin F. Yang, Mengdi Wang
Feature-Based Q-Learning for Two-Player Stochastic Games. CoRR (2019)
Vladimir Braverman, Moses Charikar, William Kuszmaul, David P. Woodruff, Lin F. Yang
The One-Way Communication Complexity of Dynamic Time Warping Distance. CoRR (2019)
Zhao Song, Ruosong Wang, Lin F. Yang, Hongyang Zhang, Peilin Zhong
Efficient Symmetric Norm Regression via Linear Sketching. NeurIPS (2019)
Lin F. Yang, Mengdi Wang
Reinforcement Leaning in Feature Space: Matrix Bandit, Kernels, and Regret Bound. CoRR (2019)
Yibo Lin, Zhao Song, Lin F. Yang
Towards a Theoretical Understanding of Hashing-Based Neural Nets. AISTATS (2019)
Lin F. Yang, Mengdi Wang
Sample-Optimal Parametric Q-Learning with Linear Transition Models. CoRR (2019)
Simon S. Du, Sham M. Kakade, Ruosong Wang, Lin F. Yang
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? CoRR (2019)
Lin F. Yang, Zheng Yu, Vladimir Braverman, Tuo Zhao, Mengdi Wang
Online Factorization and Partition of Complex Networks by Random Walk. UAI (2019)
Fei Feng, Wotao Yin, Lin F. Yang
Does Knowledge Transfer Always Help to Learn a Better Policy? CoRR (2019)
Lin F. Yang, Chengzhuo Ni, Mengdi Wang
Learning to Control in Metric Space with Optimal Regret. CoRR (2019)
Vladimir Braverman, Emanuele Viola, David P. Woodruff, Lin F. Yang
Revisiting Frequency Moment Estimation in Random Order Streams. CoRR (2018)
Vladimir Braverman, Emanuele Viola, David P. Woodruff, Lin F. Yang
Revisiting Frequency Moment Estimation in Random Order Streams. ICALP (2018)
Lin F. Yang, Raman Arora, Vladimir Braverman, Tuo Zhao
The Physical Systems Behind Optimization Algorithms. NeurIPS (2018)
Vladimir Braverman, Stephen R. Chestnut, Robert Krauthgamer, Yi Li, David P. Woodruff, Lin F. Yang
Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order. ICML (2018)
Yibo Lin, Zhao Song, Lin F. Yang
Towards a Theoretical Understanding of Hashing-Based Neural Nets. CoRR (2018)
Zhao Song, Lin F. Yang, Peilin Zhong
Sensitivity Sampling Over Dynamic Geometric Data Streams with Applications to k-Clustering. CoRR (2018)
Avrim Blum, Vladimir Braverman, Ananya Kumar, Harry Lang, Lin F. Yang
Approximate Convex Hull of Data Streams. ICALP (2018)
Vladimir Braverman, Zaoxing Liu, Tejasvam Singh, N. V. Vinodchandran, Lin F. Yang
New Bounds for the CLIQUE-GAP Problem Using Graph Decomposition Theory. Algorithmica 80 (2) (2018)
Vladimir Braverman, Robert Krauthgamer, Lin F. Yang
Universal Streaming of Subset Norms. CoRR (2018)
Zhehui Chen, Xingguo Li, Lin F. Yang, Jarvis D. Haupt, Tuo Zhao
On Landscape of Lagrangian Functions and Stochastic Search for Constrained Nonconvex Optimization. CoRR (2018)
Vladimir Braverman, Gereon Frahling, Harry Lang, Christian Sohler, Lin F. Yang
Clustering High Dimensional Dynamic Data Streams. ICML (2017)
Lin F. Yang, Vladimir Braverman, Tuo Zhao, Mengdi Wang
Dynamic Factorization and Partition of Complex Networks. CoRR (2017)
Xingguo Li, Lin F. Yang, Jason Ge, Jarvis D. Haupt, Tong Zhang, Tuo Zhao
On Quadratic Convergence of DC Proximal Newton Algorithm for Nonconvex Sparse Learning in High Dimensions. CoRR (2017)
Zhuoran Yang, Lin F. Yang, Ethan X. Fang, Tuo Zhao, Zhaoran Wang, Matey Neykov
Misspecified Nonconvex Statistical Optimization for Phase Retrieval. CoRR (2017)

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Publication Activity (10 Years)

Top Topics

Top Venues

Lin F. Yang