Dylan J. Foster

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Alexander RakhlinProfessor, MITVerified email at mit.edu
Akshay KrishnamurthyUniversity of Massachusetts AmherstVerified email at cs.umass.edu
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteVerified email at ttic.edu
Ayush SekhariPostdoctoral Associate, MITVerified email at mit.edu
Haipeng LuoAssociate Professor, University of Southern CaliforniaVerified email at usc.edu
Noah GolowichMITVerified email at mit.edu
Max SimchowitzMITVerified email at andrew.cmu.edu
Nathan SrebroProfessor, TTIC and University of ChicagoVerified email at ttic.edu
John LangfordMicrosoft Research New YorkVerified email at hunch.net
Tengyang XieAssistant Professor of Computer Science, University of Wisconsin-MadisonVerified email at cs.wisc.edu
Jian QianMITVerified email at mit.edu
Mehryar MohriHead, ML Theory, Google Research; Professor, Courant Institute of Mathematical Sciences.Verified email at google.com
Dipendra MisraStaff Research Scientist, Mosaic Team, DatabricksVerified email at databricks.com
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Matus TelgarskyCourant Institute of Mathematical Sciences, New York UniversityVerified email at nyu.edu
Sham M KakadeHarvard UniversityVerified email at seas.harvard.edu
Yair CarmonTel Aviv UniversityVerified email at tauex.tau.ac.il
John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford UniversityVerified email at stanford.edu
Blake WoodworthGeorge Washington UniversityVerified email at gwu.edu
Zakaria MhammediGoogle ResearchVerified email at mit.edu

Dylan J. Foster

Principal Researcher, Microsoft Research

Verified email at microsoft.com - Homepage

Machine Learning Optimization Statistics Reinforcement Learning Deep Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems 30, 2017	1435	2017
Lower bounds for non-convex stochastic optimization Y Arjevani, Y Carmon, JC Duchi, DJ Foster, N Srebro, B Woodworth Mathematical Programming 199 (1), 165-214, 2023	404	2023
Orthogonal statistical learning DJ Foster, V Syrgkanis The Annals of Statistics 51 (3), 879-908, 2023	281	2023
Beyond ucb: Optimal and efficient contextual bandits with regression oracles D Foster, A Rakhlin International conference on machine learning, 3199-3210, 2020	245	2020
Naive exploration is optimal for online lqr M Simchowitz, D Foster International Conference on Machine Learning, 8937-8948, 2020	232	2020
The statistical complexity of interactive decision making DJ Foster, SM Kakade, J Qian, A Rakhlin arXiv preprint arXiv:2112.13487, 2021	222	2021
Independent policy gradient methods for competitive reinforcement learning C Daskalakis, DJ Foster, N Golowich Advances in neural information processing systems 33, 5527-5540, 2020	208	2020
Practical contextual bandits with regression oracles D Foster, A Agarwal, M Dudik, H Luo, R Schapire International Conference on Machine Learning, 1539-1548, 2018	149	2018
Learning in games: Robustness of fast convergence DJ Foster, Z Li, T Lykouris, K Sridharan, E Tardos Advances In Neural Information Processing Systems, 4734-4742, 2016	133	2016
Adapting to misspecification in contextual bandits DJ Foster, C Gentile, M Mohri, J Zimmert Advances in Neural Information Processing Systems 33, 11478-11489, 2020	119	2020
Model selection for contextual bandits DJ Foster, A Krishnamurthy, H Luo Advances in Neural Information Processing Systems 32, 2019	111	2019
Instance-dependent complexity of contextual bandits and reinforcement learning: A disagreement-based perspective DJ Foster, A Rakhlin, D Simchi-Levi, Y Xu arXiv preprint arXiv:2010.03104, 2020	104	2020
Logistic regression: The importance of being improper DJ Foster, S Kale, H Luo, M Mohri, K Sridharan Conference on learning theory, 167-208, 2018	101	2018
Uniform convergence of gradients for non-convex learning and optimization DJ Foster, A Sekhari, K Sridharan Advances in neural information processing systems 31, 2018	90	2018
Logarithmic regret for adversarial online control D Foster, M Simchowitz International Conference on Machine Learning, 3211-3221, 2020	87	2020
Learning nonlinear dynamical systems from a single trajectory D Foster, T Sarkar, A Rakhlin Learning for Dynamics and Control, 851-861, 2020	86	2020
The role of coverage in online reinforcement learning T Xie, DJ Foster, Y Bai, N Jiang, SM Kakade arXiv preprint arXiv:2210.04157, 2022	85	2022
Parameter-free online learning via model selection DJ Foster, S Kale, M Mohri, K Sridharan Advances in Neural Information Processing Systems 30, 2017	75	2017
Offline reinforcement learning: Fundamental barriers for value function approximation DJ Foster, A Krishnamurthy, D Simchi-Levi, Y Xu arXiv preprint arXiv:2111.10919, 2021	71	2021
Second-order information in non-convex stochastic optimization: Power and limitations Y Arjevani, Y Carmon, JC Duchi, DJ Foster, A Sekhari, K Sridharan Conference on Learning Theory, 242-299, 2020	65	2020

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