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Lin Yang 0011
Person information
- affiliation: University of California, Los Angeles, CA, USA
Other persons with the same name
- Lin Yang — disambiguation page
- Lin Yang 0001 — University of Manchester, UK
- Lin Yang 0002 — University of Florida, Gainesville, FL, USA
- Lin Yang 0003
— University of Notre Dame, IN, USA
- Lin Yang 0004
— University of Electronic Science and Technology of China, National Key Laboratory of Science and Technology on Communications, Chengdu, China
- Lin Yang 0005
— Wuhan University, School of Electrical Engineering and Automation, China
- Lin Yang 0006
— Xidian University, State Key Discipline Laboratory of Wide Bandgap Semiconductor Technology, Xian, China
- Lin Yang 0007
— China University of Geoscience, Faculty of Information Engineering, Wuhan, China
- Lin Yang 0008
— Wuhan University of Technology, School of Logistics Engineering, China
- Lin Yang 0009 — Huawei Noah's Ark Lab, China (and 1 more)
- Lin Yang 0010 — University of Minnesota, Department of Electrical and Computer Engineering, MN, USA
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2020 – today
- 2024
- [j9]Andrea Soltoggio
, Eseoghene Ben-Iwhiwhu
, Vladimir Braverman, Eric Eaton, Benjamin Epstein, Yunhao Ge, Lucy Halperin, Jonathan P. How, Laurent Itti, Michael A. Jacobs
, Pavan Kantharaju, Long Le
, Steven Lee, Xinran Liu, Sildomar T. Monteiro
, David Musliner, Saptarshi Nath
, Priyadarshini Panda, Christos Peridis, Hamed Pirsiavash, Vishwa S. Parekh, Kaushik Roy
, Shahaf S. Shperberg, Hava T. Siegelmann
, Peter Stone
, Kyle Vedder, Jingfeng Wu
, Lin Yang, Guangyao Zheng, Soheil Kolouri:
A collective AI via lifelong learning and sharing at the edge. Nat. Mac. Intell. 6(3): 251-264 (2024) - [j8]Outongyi Lv, Bingxin Zhou
, Lin F. Yang
:
Modeling Bellman-error with logistic distribution with applications in reinforcement learning. Neural Networks 177: 106387 (2024) - [c69]Jiayi Huang, Han Zhong, Liwei Wang, Lin Yang:
Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximation. AISTATS 2024: 3673-3681 - [c68]Osama A. Hanna, Merve Karakas, Lin Yang, Christina Fragouli:
Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels. AISTATS 2024: 3898-3906 - [c67]Jialin Dong, Jiayi Wang, Lin F. Yang:
Provably Correct SGD-Based Exploration for Generalized Stochastic Bandit Problem. SmartNets 2024: 1-6 - [i78]Jialin Dong, Bahare Fatemi, Bryan Perozzi, Lin F. Yang, Anton Tsitsulin:
Don't Forget to Connect! Improving RAG with Graph-based Reranking. CoRR abs/2405.18414 (2024) - [i77]Osama A. Hanna, Merve Karakas, Lin F. Yang, Christina Fragouli:
Learning for Bandits under Action Erasures. CoRR abs/2406.18072 (2024) - [i76]Tian Tian, Lin F. Yang, Csaba Szepesvári:
Confident Natural Policy Gradient for Local Planning in qπ-realizable Constrained MDPs. CoRR abs/2406.18529 (2024) - [i75]Ally Yalei Du, Lin F. Yang, Ruosong Wang:
Misspecified Q-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error. CoRR abs/2407.13622 (2024) - 2023
- [j7]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: 175:1-175:53 (2023) - [c66]Masoud Monajatipoor, Liunian Harold Li, Mozhdeh Rouhsedaghat, Lin Yang, Kai-Wei Chang:
MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models. ACL (2) 2023: 495-508 - [c65]Osama A. Hanna, Lin Yang, Christina Fragouli:
Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms. COLT 2023: 1791-1821 - [c64]Sanae Amani, Lin Yang, Ching-An Cheng:
Provably Efficient Lifelong Reinforcement Learning with Linear Representation. ICLR 2023 - [c63]Sanae Amani, Tor Lattimore, András György, Lin Yang:
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. ICML 2023: 691-717 - [c62]Jialin Dong, Lin Yang:
Does Sparsity Help in Learning Misspecified Linear Bandits? ICML 2023: 8317-8333 - [c61]Osama A. Hanna, Merve Karakas, Lin F. Yang, Christina Fragouli:
Multi-Arm Bandits over Action Erasure Channels. ISIT 2023: 1312-1317 - [c60]Osama A. Hanna, Lin Yang, Christina Fragouli:
Efficient Batched Algorithm for Contextual Linear Bandits with Large Action Space via Soft Elimination. NeurIPS 2023 - [c59]Jiayi Huang, Han Zhong, Liwei Wang, Lin Yang:
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds. NeurIPS 2023 - [c58]Amin Karbasi, Grigoris Velegkas, Lin Yang, Felix Zhou:
Replicability in Reinforcement Learning. NeurIPS 2023 - [i74]Jialin Dong, Lin F. Yang:
Does Sparsity Help in Learning Misspecified Linear Bandits? CoRR abs/2303.16998 (2023) - [i73]Dingwen Kong, Lin F. Yang:
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning. CoRR abs/2304.08944 (2023) - [i72]Amin Karbasi, Grigoris Velegkas, Lin F. Yang, Felix Zhou:
Replicability in Reinforcement Learning. CoRR abs/2305.19562 (2023) - [i71]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 abs/2306.01311 (2023) - [i70]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 abs/2306.06836 (2023) - [i69]Sanae Amani, Khushbu Pahwa, Vladimir Braverman, Lin F. Yang:
Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing. CoRR abs/2307.05834 (2023) - [i68]Haochen Zhang, Xi Chen, Lin F. Yang:
Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning. CoRR abs/2309.10129 (2023) - [i67]Jiayi Huang, Han Zhong, Liwei Wang, Lin F. Yang:
Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximation. CoRR abs/2312.04464 (2023) - [i66]Osama A. Hanna, Merve Karakas, Lin F. Yang, Christina Fragouli:
Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels. CoRR abs/2312.14259 (2023) - 2022
- [j6]Osama A. Hanna
, Lin F. Yang, Christina Fragouli
:
Compression for Multi-Arm Bandits. IEEE J. Sel. Areas Inf. Theory 3(4): 773-788 (2022) - [j5]Vladimir Braverman, Robert Krauthgamer, Lin F. Yang:
Universal Streaming of Subset Norms. Theory Comput. 18: 1-32 (2022) - [c57]Jingfeng Wu, Vladimir Braverman, Lin Yang
:
Gap-Dependent Unsupervised Exploration for Reinforcement Learning. AISTATS 2022: 4109-4131 - [c56]Osama A. Hanna, Lin Yang
, Christina Fragouli:
Solving Multi-Arm Bandit Using a Few Bits of Communication. AISTATS 2022: 11215-11236 - [c55]Sanae Amani, Lin F. Yang
:
Doubly Pessimistic Algorithms for Strictly Safe Off-Policy Optimization. CISS 2022: 113-118 - [c54]Xiaoyu Chen, Jiachen Hu, Lin Yang
, Liwei Wang:
Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver. ICLR 2022 - [c53]Weichao Mao, Lin Yang
, Kaiqing Zhang, Tamer Basar:
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning. ICML 2022: 15007-15049 - [c52]Osama A. Hanna, Lin Yang, Christina Fragouli:
Learning from Distributed Users in Contextual Linear Bandits Without Sharing the Context. NeurIPS 2022 - [c51]Dingwen Kong, Lin Yang:
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning. NeurIPS 2022 - [c50]Sharan Vaswani, Lin Yang, Csaba Szepesvári:
Near-Optimal Sample Complexity Bounds for Constrained MDPs. NeurIPS 2022 - [i65]Sanae Amani, Tor Lattimore, András György, Lin F. Yang
:
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. CoRR abs/2205.13170 (2022) - [i64]Sanae Amani, Lin F. Yang
, Ching-An Cheng:
Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximation. CoRR abs/2206.00270 (2022) - [i63]Osama A. Hanna, Lin F. Yang
, Christina Fragouli:
Learning in Distributed Contextual Linear Bandits Without Sharing the Context. CoRR abs/2206.04180 (2022) - [i62]Sharan Vaswani, Lin F. Yang
, Csaba Szepesvári:
Near-Optimal Sample Complexity Bounds for Constrained MDPs. CoRR abs/2206.06270 (2022) - [i61]Ningyuan Huang, Soledad Villar, Carey E. Priebe, Da Zheng, Chengyue Huang, Lin Yang
, Vladimir Braverman:
From Local to Global: Spectral-Inspired Graph Neural Networks. CoRR abs/2209.12054 (2022) - [i60]Osama A. Hanna, Lin F. Yang
, Christina Fragouli:
Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms. CoRR abs/2211.05632 (2022) - [i59]Jinghan Wang, Mengdi Wang
, Lin F. Yang
:
Near Sample-Optimal Reduction-based Policy Learning for Average Reward MDP. CoRR abs/2212.00603 (2022) - 2021
- [c49]Junhong Shen, Lin F. Yang:
Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation. AAAI 2021: 9558-9566 - [c48]Kunhe Yang, Lin F. Yang
, Simon S. Du:
Q-learning with Logarithmic Regret. AISTATS 2021: 1576-1584 - [c47]Yuanzhi Li, Ruosong Wang, Lin F. Yang
:
Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning. FOCS 2021: 965-976 - [c46]Sanae Amani, Christos Thrampoulidis, Lin Yang
:
Safe Reinforcement Learning with Linear Function Approximation. ICML 2021: 243-253 - [c45]Fei Feng, Wotao Yin, Alekh Agarwal, Lin Yang
:
Provably Correct Optimization and Exploration with Non-linear Policies. ICML 2021: 3263-3273 - [c44]Haque Ishfaq, Qiwen Cui, Viet Nguyen, Alex Ayoub, Zhuoran Yang, Zhaoran Wang, Doina Precup, Lin Yang:
Randomized Exploration in Reinforcement Learning with General Value Function Approximation. ICML 2021: 4607-4616 - [c43]Jialin Dong, Da Zheng, Lin F. Yang
, George Karypis:
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs. KDD 2021: 289-299 - [c42]Nived Rajaraman, Yanjun Han, Lin Yang
, Jingbo Liu, Jiantao Jiao, Kannan Ramchandran:
On the Value of Interaction and Function Approximation in Imitation Learning. NeurIPS 2021: 1325-1336 - [c41]Jingfeng Wu, Vladimir Braverman, Lin Yang
:
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning. NeurIPS 2021: 13112-13124 - [c40]Han Zhong, Jiayi Huang, Lin Yang
, Liwei Wang:
Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs. NeurIPS 2021: 15710-15720 - [c39]Qiwen Cui, Lin F. Yang
:
Minimax sample complexity for turn-based stochastic game. UAI 2021: 1496-1504 - [i58]Minbo Gao, Tianle Xie, Simon S. Du, Lin F. Yang:
A Provably Efficient Algorithm for Linear Markov Decision Process with Low Switching Cost. CoRR abs/2101.00494 (2021) - [i57]Nived Rajaraman, Yanjun Han, Lin F. Yang, Kannan Ramchandran, Jiantao Jiao:
Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally. CoRR abs/2102.12948 (2021) - [i56]Fei Feng, Wotao Yin, Alekh Agarwal, Lin F. Yang:
Provably Correct Optimization and Exploration with Non-linear Policies. CoRR abs/2103.11559 (2021) - [i55]Jialin Dong, Da Zheng, Lin F. Yang, George Karypis:
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs. CoRR abs/2106.06150 (2021) - [i54]Sanae Amani, Christos Thrampoulidis, Lin F. Yang:
Safe Reinforcement Learning with Linear Function Approximation. CoRR abs/2106.06239 (2021) - [i53]Dingwen Kong, Ruslan Salakhutdinov, Ruosong Wang, Lin F. Yang:
Online Sub-Sampling for Reinforcement Learning with General Function Approximation. CoRR abs/2106.07203 (2021) - [i52]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 abs/2106.07841 (2021) - [i51]Jingfeng Wu, Vladimir Braverman, Lin F. Yang:
Gap-Dependent Unsupervised Exploration for Reinforcement Learning. CoRR abs/2108.05439 (2021) - [i50]Xiaoyu Chen, Jiachen Hu, Lin F. Yang, Liwei Wang:
Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver. CoRR abs/2110.03244 (2021) - [i49]Junhong Shen, Lin F. Yang:
Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation. CoRR abs/2110.04422 (2021) - [i48]Weichao Mao, Tamer Basar, Lin F. Yang, Kaiqing Zhang:
Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration. CoRR abs/2110.05707 (2021) - [i47]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 abs/2110.13876 (2021) - [i46]Yuanzhi Li, Ruosong Wang, Lin F. Yang:
Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning. CoRR abs/2111.00633 (2021) - [i45]Osama A. Hanna, Lin F. Yang, Christina Fragouli:
Solving Multi-Arm Bandit Using a Few Bits of Communication. CoRR abs/2111.06067 (2021) - 2020
- [j4]Vladimir Braverman, Moses Charikar
, William Kuszmaul, Lin F. Yang
:
The one-way communication complexity of dynamic time warping distance. J. Comput. Geom. 11(2): 62-93 (2020) - [c38]Yingyu Liang, Zhao Song, Mengdi Wang, Lin Yang
, Xin Yang:
Sketching Transformed Matrices with Applications to Natural Language Processing. AISTATS 2020: 467-481 - [c37]Aaron Sidford, Mengdi Wang, Lin Yang
, Yinyu Ye:
Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity. AISTATS 2020: 2992-3002 - [c36]Alekh Agarwal, Sham M. Kakade, Lin F. Yang
:
Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal. COLT 2020: 67-83 - [c35]Simon S. Du, Sham M. Kakade, Ruosong Wang, Lin F. Yang
:
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? ICLR 2020 - [c34]Alex Ayoub, Zeyu Jia, Csaba Szepesvári, Mengdi Wang, Lin Yang
:
Model-Based Reinforcement Learning with Value-Targeted Regression. ICML 2020: 463-474 - [c33]Jingfeng Wu, Vladimir Braverman, Lin Yang
:
Obtaining Adjustable Regularization for Free via Iterate Averaging. ICML 2020: 10344-10354 - [c32]Lin Yang
, Mengdi Wang:
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound. ICML 2020: 10746-10756 - [c31]Zeyu Jia, Lin Yang
, Csaba Szepesvári, Mengdi Wang:
Model-Based Reinforcement Learning with Value-Targeted Regression. L4DC 2020: 666-686 - [c30]Qiwen Cui, Lin F. Yang
:
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning? NeurIPS 2020 - [c29]Fei Feng, Ruosong Wang, Wotao Yin, Simon S. Du, Lin F. Yang
:
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning. NeurIPS 2020 - [c28]Nived Rajaraman, Lin F. Yang
, Jiantao Jiao, Kannan Ramchandran:
Toward the Fundamental Limits of Imitation Learning. NeurIPS 2020 - [c27]Ruosong Wang, Simon S. Du, Lin F. Yang
, Sham M. Kakade:
Is Long Horizon RL More Difficult Than Short Horizon RL? NeurIPS 2020 - [c26]Ruosong Wang, Simon S. Du, Lin F. Yang
, Ruslan Salakhutdinov:
On Reward-Free Reinforcement Learning with Linear Function Approximation. NeurIPS 2020 - [c25]Ruosong Wang, Ruslan Salakhutdinov, Lin F. Yang
:
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension. NeurIPS 2020 - [c24]Ruosong Wang, Peilin Zhong, Simon S. Du, Ruslan Salakhutdinov, Lin F. Yang
:
Planning with General Objective Functions: Going Beyond Total Rewards. NeurIPS 2020 - [c23]Yichong Xu, Ruosong Wang, Lin F. Yang
, Aarti Singh, Artur Dubrawski:
Preference-based Reinforcement Learning with Finite-Time Guarantees. NeurIPS 2020 - [c22]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 - [i44]Yingyu Liang, Zhao Song, Mengdi Wang, Lin F. Yang, Xin Yang:
Sketching Transformed Matrices with Applications to Natural Language Processing. CoRR abs/2002.09812 (2020) - [i43]Gabriel I. Fernandez, Colin Togashi, Dennis W. Hong, Lin F. Yang:
Deep Reinforcement Learning with Linear Quadratic Regulator Regions. CoRR abs/2002.09820 (2020) - [i42]Fei Feng, Ruosong Wang, Wotao Yin, Simon S. Du, Lin F. Yang:
Provably Efficient Exploration for RL with Unsupervised Learning. CoRR abs/2003.06898 (2020) - [i41]Ruosong Wang, Simon S. Du, Lin F. Yang, Sham M. Kakade:
Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning? CoRR abs/2005.00527 (2020) - [i40]Ruosong Wang, Ruslan Salakhutdinov, Lin F. Yang:
Provably Efficient Reinforcement Learning with General Value Function Approximation. CoRR abs/2005.10804 (2020) - [i39]Alex Ayoub, Zeyu Jia, Csaba Szepesvári, Mengdi Wang
, Lin F. Yang:
Model-Based Reinforcement Learning with Value-Targeted Regression. CoRR abs/2006.01107 (2020) - [i38]Yichong Xu, Ruosong Wang, Lin F. Yang, Aarti Singh, Artur Dubrawski:
Preference-based Reinforcement Learning with Finite-Time Guarantees. CoRR abs/2006.08910 (2020) - [i37]Kunhe Yang, Lin F. Yang, Simon S. Du:
Q-learning with Logarithmic Regret. CoRR abs/2006.09118 (2020) - [i36]Ruosong Wang, Simon S. Du, Lin F. Yang, Ruslan Salakhutdinov:
On Reward-Free Reinforcement Learning with Linear Function Approximation. CoRR abs/2006.11274 (2020) - [i35]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 abs/2007.07461 (2020) - [i34]Jingfeng Wu, Vladimir Braverman, Lin F. Yang:
Obtaining Adjustable Regularization for Free via Iterate Averaging. CoRR abs/2008.06736 (2020) - [i33]Nived Rajaraman, Lin F. Yang, Jiantao Jiao, Kannan Ramchandran:
Toward the Fundamental Limits of Imitation Learning. CoRR abs/2009.05990 (2020) - [i32]Qiwen Cui, Lin F. Yang:
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning? CoRR abs/2010.05673 (2020) - [i31]Tianyu Wang, Lin F. Yang, Zizhuo Wang:
Random Walk Bandits. CoRR abs/2011.01445 (2020) - [i30]Tianyu Wang, Lin F. Yang:
Episodic Linear Quadratic Regulators with Low-rank Transitions. CoRR abs/2011.01568 (2020) - [i29]Jingfeng Wu, Vladimir Braverman, Lin F. Yang:
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning. CoRR abs/2011.13034 (2020) - [i28]Qiwen Cui, Lin F. Yang:
Minimax Sample Complexity for Turn-based Stochastic Game. CoRR abs/2011.14267 (2020)
2010 – 2019
- 2019
- [j3]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): 545-571 (2019) - [c21]Yibo Lin, Zhao Song, Lin F. Yang
:
Towards a Theoretical Understanding of Hashing-Based Neural Nets. AISTATS 2019: 127-137 - [c20]Zhehui Chen, Xingguo Li, Lin Yang, Jarvis D. Haupt, Tuo Zhao:
On Constrained Nonconvex Stochastic Optimization: A Case Study for Generalized Eigenvalue Decomposition. AISTATS 2019: 916-925 - [c19]Chengzhuo Ni, Lin F. Yang
, Mengdi Wang
:
Learning to Control in Metric Space with Optimal Regret. Allerton 2019: 726-733 - [c18]Vladimir Braverman, Moses Charikar
, William Kuszmaul, David P. Woodruff, Lin F. Yang
:
The One-Way Communication Complexity of Dynamic Time Warping Distance. SoCG 2019: 16:1-16:15 - [c17]Lin Yang
, Mengdi Wang:
Sample-Optimal Parametric Q-Learning Using Linearly Additive Features. ICML 2019: 6995-7004 - [c16]Zhao Song, Ruosong Wang, Lin F. Yang
, Hongyang Zhang, Peilin Zhong:
Efficient Symmetric Norm Regression via Linear Sketching. NeurIPS 2019: 828-838 - [c15]Lin F. Yang
, Zheng Yu, Vladimir Braverman, Tuo Zhao, Mengdi Wang:
Online Factorization and Partition of Complex Networks by Random Walk. UAI 2019: 820-830 - [i27]Lin F. Yang, Mengdi Wang:
Sample-Optimal Parametric Q-Learning with Linear Transition Models. CoRR abs/1902.04779 (2019) - [i26]Vladimir Braverman, Moses Charikar, William Kuszmaul, David P. Woodruff, Lin F. Yang:
The One-Way Communication Complexity of Dynamic Time Warping Distance. CoRR abs/1903.03520 (2019) - [i25]Lin F. Yang, Chengzhuo Ni, Mengdi Wang:
Learning to Control in Metric Space with Optimal Regret. CoRR abs/1905.01576 (2019) - [i24]Lin F. Yang, Mengdi Wang:
Reinforcement Leaning in Feature Space: Matrix Bandit, Kernels, and Regret Bound. CoRR abs/1905.10389 (2019) - [i23]Zeyu Jia, Lin F. Yang, Mengdi Wang:
Feature-Based Q-Learning for Two-Player Stochastic Games. CoRR abs/1906.00423 (2019) - [i22]Alekh Agarwal, Sham M. Kakade, Lin F. Yang:
On the Optimality of Sparse Model-Based Planning for Markov Decision Processes. CoRR abs/1906.03804 (2019) - [i21]Aaron Sidford, Mengdi Wang, Lin F. Yang, Yinyu Ye:
Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity. CoRR abs/1908.11071 (2019) - [i20]Zhao Song, Ruosong Wang, Lin F. Yang, Hongyang Zhang, Peilin Zhong:
Efficient Symmetric Norm Regression via Linear Sketching. CoRR abs/1910.01788 (2019) - [i19]Simon S. Du, Sham M. Kakade, Ruosong Wang, Lin F. Yang:
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? CoRR abs/1910.03016 (2019) - [i18]Simon S. Du, Ruosong Wang, Mengdi Wang, Lin F. Yang:
Continuous Control with Contexts, Provably. CoRR abs/1910.13614 (2019) - [i17]Fei Feng, Wotao Yin, Lin F. Yang:
Does Knowledge Transfer Always Help to Learn a Better Policy? CoRR abs/1912.02986 (2019) - 2018
- [j2]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): 652-667 (2018) - [j1]Nikita Ivkin, Zaoxing Liu
, Lin F. Yang
, S. S. Kumar, Gerard Lemson, Mark Neyrinck
, Alexander S. Szalay
, Vladimir Braverman, Tamas Budavari
:
Scalable streaming tools for analyzing N-body simulations: Finding halos and investigating excursion sets in one pass. Astron. Comput. 23: 166-179 (2018) - [c14]Avrim Blum, Vladimir Braverman, Ananya Kumar, Harry Lang, Lin F. Yang
:
Approximate Convex Hull of Data Streams. ICALP 2018: 21:1-21:13 - [c13]Vladimir Braverman, Emanuele Viola, David P. Woodruff, Lin F. Yang
:
Revisiting Frequency Moment Estimation in Random Order Streams. ICALP 2018: 25:1-25:14 - [c12]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: 648-657 - [c11]Minshuo Chen, Lin Yang
, Mengdi Wang, Tuo Zhao:
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization. NeurIPS 2018: 3500-3510 - [c10]Lin F. Yang
, Raman Arora, Vladimir Braverman, Tuo Zhao:
The Physical Systems Behind Optimization Algorithms. NeurIPS 2018: 4377-4386 - [c9]Aaron Sidford, Mengdi Wang, Xian Wu, Lin Yang
, Yinyu Ye:
Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model. NeurIPS 2018: 5192-5202 - [i16]Zhao Song, Lin F. Yang, Peilin Zhong:
Sensitivity Sampling Over Dynamic Geometric Data Streams with Applications to k-Clustering. CoRR abs/1802.00459 (2018) - [i15]Vladimir Braverman, Emanuele Viola, David P. Woodruff, Lin F. Yang:
Revisiting Frequency Moment Estimation in Random Order Streams. CoRR abs/1803.02270 (2018) - [i14]Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao:
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization. CoRR abs/1803.02312 (2018) - [i13]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 abs/1806.05151 (2018) - [i12]Vladimir Braverman, Robert Krauthgamer, Lin F. Yang:
Universal Streaming of Subset Norms. CoRR abs/1812.00241 (2018) - [i11]Yibo Lin, Zhao Song, Lin F. Yang:
Towards a Theoretical Understanding of Hashing-Based Neural Nets. CoRR abs/1812.10244 (2018) - 2017
- [c8]Vladimir Braverman, Gereon Frahling, Harry Lang, Christian Sohler
, Lin F. Yang
:
Clustering High Dimensional Dynamic Data Streams. ICML 2017: 576-585 - [c7]Zhehui Chen, Lin F. Yang
, Chris Junchi Li, Tuo Zhao:
Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability. ICML 2017: 777-786 - [c6]Xingguo Li, Lin Yang, Jason Ge, Jarvis D. Haupt, Tong Zhang, Tuo Zhao:
On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning. NIPS 2017: 2742-2752 - [c5]Jaroslaw Blasiok
, Vladimir Braverman, Stephen R. Chestnut, Robert Krauthgamer
, Lin F. Yang
:
Streaming symmetric norms via measure concentration. STOC 2017: 716-729 - [i10]Lin F. Yang, Vladimir Braverman, Tuo Zhao, Mengdi Wang:
Dynamic Factorization and Partition of Complex Networks. CoRR abs/1705.07881 (2017) - [i9]Vladimir Braverman, Gereon Frahling, Harry Lang, Christian Sohler, Lin F. Yang:
Clustering High Dimensional Dynamic Data Streams. CoRR abs/1706.03887 (2017) - [i8]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 abs/1706.06066 (2017) - [i7]Avrim Blum, Vladimir Braverman, Ananya Kumar, Harry Lang, Lin F. Yang:
Approximate Convex Hull of Data Streams. CoRR abs/1712.04564 (2017) - [i6]Zhuoran Yang, Lin F. Yang, Ethan X. Fang, Tuo Zhao, Zhaoran Wang, Matey Neykov:
Misspecified Nonconvex Statistical Optimization for Phase Retrieval. CoRR abs/1712.06245 (2017) - 2016
- [c4]Vladimir Braverman, Stephen R. Chestnut, David P. Woodruff, Lin F. Yang
:
Streaming Space Complexity of Nearly All Functions of One Variable on Frequency Vectors. PODS 2016: 261-276 - [i5]Vladimir Braverman, Stephen R. Chestnut, David P. Woodruff, Lin F. Yang:
Streaming Space Complexity of Nearly All Functions of One Variable on Frequency Vectors. CoRR abs/1601.07473 (2016) - [i4]Vladimir Braverman, Stephen R. Chestnut, Robert Krauthgamer, Lin F. Yang:
Sketches for Matrix Norms: Faster, Smaller and More General. CoRR abs/1609.05885 (2016) - [i3]Lin F. Yang, Raman Arora, Vladimir Braverman, Tuo Zhao:
The Physical Systems Behind Optimization Algorithms. CoRR abs/1612.02803 (2016) - 2015
- [c3]Zaoxing Liu, Nikita Ivkin, Lin Yang
, Mark Neyrinck
, Gerard Lemson, Alexander S. Szalay, Vladimir Braverman, Tamas Budavari
, Randal C. Burns
, Xin Wang:
Streaming Algorithms for Halo Finders. e-Science 2015: 342-351 - [c2]Vladimir Braverman, Zaoxing Liu, Tejasvam Singh, N. V. Vinodchandran, Lin F. Yang:
New Bounds for the CLIQUE-GAP Problem Using Graph Decomposition Theory. MFCS (2) 2015: 151-162 - [i2]Vladimir Braverman, Stephen R. Chestnut, Robert Krauthgamer, Lin F. Yang:
Streaming Symmetric Norms via Measure Concentration. CoRR abs/1511.01111 (2015) - 2014
- [c1]Diptarka Chakraborty, Aduri Pavan, Raghunath Tewari, N. V. Vinodchandran, Lin F. Yang
:
New Time-Space Upperbounds for Directed Reachability in High-genus and H-minor-free Graphs. FSTTCS 2014: 585-595 - [i1]Diptarka Chakraborty, Aduri Pavan, Raghunath Tewari, N. Variyam Vinodchandran, Lin F. Yang
:
New Time-Space Upperbounds for Directed Reachability in High-genus and H-minor-free Graphs. Electron. Colloquium Comput. Complex. TR14 (2014) - 2013
- [b1]Lin Yang
:
Mathematical Theories of Interaction with Oracles. Carnegie Mellon University, USA, 2013
Coauthor Index
![](https://dblp.uni-trier.de/img/cog.dark.24x24.png)
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