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 KakadeHarvard UniversityVerified email at seas.harvard.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
Joe SilkIAP & JHUVerified email at iap.fr
Miguel Angel Aragon CalvoUNAMVerified email at astro.unam.mx
Zhehui ChenDidi LabsVerified 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
Jarvis HauptAssociate Professor of Electrical and Computer Engineering, University of MinnesotaVerified email at umn.edu
Xingguo LiRadix TradingVerified email at radix-trading.com
Matey NeykovAssistant Professor, Department of Statistics & Data Science, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Ethan X. FangAssistant Professor at Duke UniversityVerified email at duke.edu
Christian SohlerProfessor for Algorithmic Data Analysis, University of CologneVerified email at uni-koeln.de

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
Sample-optimal parametric q-learning using linearly additive features L Yang, M Wang International Conference on Machine Learning, 6995-7004, 2019	168*	2019
Reinforcement learning in feature space: Matrix bandit, kernels, and regret bound L Yang, M Wang International Conference on Machine Learning, 10746-10756, 2020	162	2020
Near-optimal time and sample complexities for solving Markov decision processes with a generative model A Sidford, M Wang, X Wu, L Yang, Y Ye Advances in Neural Information Processing Systems 31, 2018	157*	2018
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	128	2020
Is a good representation sufficient for sample efficient reinforcement learning? SS Du, SM Kakade, R Wang, LF Yang arXiv preprint arXiv:1910.03016, 2019	112	2019
Model-based reinforcement learning with a generative model is minimax optimal A Agarwal, S Kakade, LF Yang Conference on Learning Theory, 67-83, 2020	102*	2020
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	92	2014
Model-based multi-agent rl in zero-sum markov games with near-optimal sample complexity K Zhang, S Kakade, T Basar, L Yang Advances in Neural Information Processing Systems 33, 1166-1178, 2020	57	2020
Reinforcement learning with general value function approximation: Provably efficient approach via bounded eluder dimension R Wang, RR Salakhutdinov, L Yang Advances in Neural Information Processing Systems 33, 6123-6135, 2020	54	2020
On reward-free reinforcement learning with linear function approximation R Wang, SS Du, L Yang, RR Salakhutdinov Advances in neural information processing systems 33, 17816-17826, 2020	49	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	46	2015
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	45	2020
Provably efficient reinforcement learning with general value function approximation R Wang, R Salakhutdinov, LF Yang	43	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	34	2017
Feature-based q-learning for two-player stochastic games Z Jia, LF Yang, M Wang arXiv preprint arXiv:1906.00423, 2019	33	2019
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	31*	2019
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	31	2013
Q-learning with logarithmic regret K Yang, L Yang, S Du International Conference on Artificial Intelligence and Statistics, 1576-1584, 2021	30	2021
Variance reduction methods for sublinear reinforcement learning S Kakade, M Wang, LF Yang	30	2018
The Optical Counterpart of NGC 1313 X-1 L Yang, H Feng, P Kaaret The Astrophysical Journal 733 (2), 118, 2011	30	2011

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