Lin F. Yang (杨林)

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Vladimir BravermanAssociate Professor of Computer Science, Johns Hopkins UniversityVerified email at cs.jhu.edu
Mengdi WangAssociate Professor at Princeton University and DeepMindVerified email at princeton.edu
Tuo ZhaoAssistant Professor, Georgia TechVerified email at gatech.edu
Sham M KakadeUniversity of WashingtonVerified email at cs.washington.edu
Mark NeyrinckUniversity of the Basque Country, Bilbao, SpainVerified email at ehu.eus
Robert KrauthgamerWeizmann Institute of ScienceVerified email at weizmann.ac.il
David WoodruffCarnegie Mellon UniversityVerified email at cs.cmu.edu
A. S. SzalayBlooomberg Distinguished Professor, Johns Hopkins UniversityVerified email at jhu.edu
Miguel Angel Aragon CalvoUNAMVerified email at astro.unam.mx
Joe SilkIAP & JHUVerified email at iap.fr
Zhehui ChenPh.D. student of ISyE, Georgia TechVerified email at gatech.edu
Zaoxing LiuAssistant Professor, Boston UniversityVerified email at bu.edu
Zhaoran WangAssistant Professor at Northwestern UniversityVerified email at northwestern.edu
Tamás BudaváriDept. of Applied Mathematics and Statistics, The Johns Hopkins UniversityVerified email at jhu.edu
Nikita IvkinAmazon.comVerified email at amazon.com
Matey NeykovAssistant Professor, Department of Statistics & Data Science, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Ethan X. FangPennsyvania State UniversityVerified email at psu.edu
Jarvis HauptAssociate Professor of Electrical and Computer Engineering, University of MinnesotaVerified email at umn.edu
Xingguo LiRadix TradingVerified email at radix-trading.com
Randal BurnsProfessor of Computer Science, Johns Hopkins UniversityVerified email at cs.jhu.edu

Lin F. Yang (杨林)

Assistant Professor, Department of Electrical and Computer Engineering @ UCLA

Verified email at ee.ucla.edu - Homepage

Machine Learning Reinforcement Learning Algorithm Theoretic Computer Science Astrophysics


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Near-optimal time and sample complexities for solving Markov decision processes with a generative model A Sidford, M Wang, X Wu, LF Yang, Y Ye Proceedings of the 32nd International Conference on Neural Information …, 2018	116*	2018
Reinforcement learning in feature space: Matrix bandit, kernels, and regret bound L Yang, M Wang International Conference on Machine Learning, 10746-10756, 2020	115	2020
Sample-optimal parametric Q-learning using linearly additive features L Yang, M Wang International Conference on Machine Learning, 6995-7004, 2019	109*	2019
The hierarchical nature of the spin alignment of dark matter haloes in filaments MA Aragon-Calvo, LF Yang Monthly Notices of the Royal Astronomical Society: Letters 440 (1), L46-L50, 2014	86	2014
Is a good representation sufficient for sample efficient reinforcement learning? SS Du, SM Kakade, R Wang, LF Yang arXiv preprint arXiv:1910.03016, 2019	75	2019
Model-based reinforcement learning with value-targeted regression A Ayoub, Z Jia, C Szepesvari, M Wang, L Yang International Conference on Machine Learning, 463-474, 2020	69	2020
Model-based reinforcement learning with a generative model is minimax optimal A Agarwal, S Kakade, LF Yang Conference on Learning Theory, 67-83, 2020	65*	2020
Warmth elevating the depths: shallower voids with warm dark matter LF Yang, MC Neyrinck, MA Aragón-Calvo, B Falck, J Silk Monthly Notices of the Royal Astronomical Society 451 (4), 3606-3614, 2015	39	2015
Provably efficient reinforcement learning with general value function approximation R Wang, R Salakhutdinov, LF Yang	36	2020
Ringing the initial Universe: the response of overdensity and transformed-density power spectra to initial spikes MC Neyrinck, LF Yang Monthly Notices of the Royal Astronomical Society 433 (2), 1628-1633, 2013	30	2013
Misspecified nonconvex statistical optimization for sparse phase retrieval Z Yang, LF Yang, EX Fang, T Zhao, Z Wang, M Neykov Mathematical Programming 176 (1), 545-571, 2019	29*	2019
Reinforcement learning with general value function approximation: Provably efficient approach via bounded eluder dimension R Wang, R Salakhutdinov, LF Yang arXiv preprint arXiv:2005.10804, 2020	27	2020
Clustering high dimensional dynamic data streams V Braverman, G Frahling, H Lang, C Sohler, LF Yang International Conference on Machine Learning, 576-585, 2017	27	2017
On reward-free reinforcement learning with linear function approximation R Wang, SS Du, LF Yang, R Salakhutdinov arXiv preprint arXiv:2006.11274, 2020	26	2020
Variance reduction methods for sublinear reinforcement learning S Kakade, M Wang, LF Yang	26	2018
New time-space upperbounds for directed reachability in high-genus and h-minor-free graphs D Chakraborty, A Pavan, R Tewari, NV Vinodchandran, LF Yang 34th International Conference on Foundation of Software Technology and …, 2014	26	2014
Streaming space complexity of nearly all functions of one variable on frequency vectors V Braverman, SR Chestnut, DP Woodruff, LF Yang Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2016	25	2016
Solving discounted stochastic two-player games with near-optimal time and sample complexity A Sidford, M Wang, L Yang, Y Ye International Conference on Artificial Intelligence and Statistics, 2992-3002, 2020	24	2020
The Optical Counterpart of NGC 1313 X-1 L Yang, H Feng, P Kaaret The Astrophysical Journal 733 (2), 118, 2011	24	2011
Model-based multi-agent rl in zero-sum markov games with near-optimal sample complexity K Zhang, SM Kakade, T Başar, LF Yang arXiv preprint arXiv:2007.07461, 2020	23	2020

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