Dylan J. Foster

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Alexander RakhlinProfessor, MITDirección de correo verificada de mit.edu
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteDirección de correo verificada de ttic.edu
Akshay KrishnamurthyUniversity of Massachusetts AmherstDirección de correo verificada de cs.umass.edu
Ayush SekhariPostdoc, MITDirección de correo verificada de mit.edu
Haipeng LuoAssociate Professor, University of Southern CaliforniaDirección de correo verificada de usc.edu
Noah GolowichMITDirección de correo verificada de mit.edu
Nathan SrebroProfessor, TTIC and University of ChicagoDirección de correo verificada de ttic.edu
John LangfordMicrosoft Research New YorkDirección de correo verificada de hunch.net
MAX SIMCHOWITZMITDirección de correo verificada de mit.edu
Mehryar MohriHead, Learning Theory Team, Google Research; Professor, Courant Institute of Mathematical Sciences.Dirección de correo verificada de google.com
Dipendra MisraStaff Research Scientist, Mosaic Team, DatabricksDirección de correo verificada de databricks.com
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyDirección de correo verificada de cs.berkeley.edu
Matus TelgarskyCourant Institute of Mathematical Sciences, New York UniversityDirección de correo verificada de nyu.edu
Jian QianMITDirección de correo verificada de mit.edu
Sham M KakadeHarvard UniversityDirección de correo verificada de seas.harvard.edu
Yair CarmonTel Aviv UniversityDirección de correo verificada de tauex.tau.ac.il
John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford UniversityDirección de correo verificada de stanford.edu
Yossi ArjevaniThe Hebrew UniversityDirección de correo verificada de mail.huji.ac.il
Blake WoodworthGeorge Washington UniversityDirección de correo verificada de gwu.edu
Satyen KaleResearch Scientist, Google ResearchDirección de correo verificada de google.com

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Dylan J. Foster

Principal Researcher, Microsoft Research

Dirección de correo verificada de microsoft.com - Página principal

Machine Learning Optimization Statistics Reinforcement Learning Deep Learning


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems 30, 2017	1291	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	322	2023
Orthogonal statistical learning DJ Foster, V Syrgkanis The Annals of Statistics 51 (3), 879-908, 2023	237	2023
Beyond ucb: Optimal and efficient contextual bandits with regression oracles D Foster, A Rakhlin International Conference on Machine Learning, 3199-3210, 2020	206	2020
Naive exploration is optimal for online lqr M Simchowitz, D Foster International Conference on Machine Learning, 8937-8948, 2020	204	2020
Independent policy gradient methods for competitive reinforcement learning C Daskalakis, DJ Foster, N Golowich Advances in neural information processing systems 33, 5527-5540, 2020	180	2020
The statistical complexity of interactive decision making DJ Foster, SM Kakade, J Qian, A Rakhlin arXiv preprint arXiv:2112.13487, 2021	177	2021
Practical contextual bandits with regression oracles D Foster, A Agarwal, M Dudík, H Luo, R Schapire International Conference on Machine Learning, 1539-1548, 2018	129	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	125	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	101	2020
Model selection for contextual bandits DJ Foster, A Krishnamurthy, H Luo Advances in Neural Information Processing Systems 32, 2019	100	2019
Logistic regression: The importance of being improper DJ Foster, S Kale, H Luo, M Mohri, K Sridharan Conference on learning theory, 167-208, 2018	92	2018
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	87	2020
Uniform convergence of gradients for non-convex learning and optimization DJ Foster, A Sekhari, K Sridharan Advances in neural information processing systems 31, 2018	82	2018
Logarithmic regret for adversarial online control D Foster, M Simchowitz International Conference on Machine Learning, 3211-3221, 2020	76	2020
Learning nonlinear dynamical systems from a single trajectory D Foster, T Sarkar, A Rakhlin Learning for Dynamics and Control, 851-861, 2020	72	2020
Parameter-free online learning via model selection DJ Foster, S Kale, M Mohri, K Sridharan Advances in Neural Information Processing Systems 30, 2017	65	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	62	2021
The role of coverage in online reinforcement learning T Xie, DJ Foster, Y Bai, N Jiang, SM Kakade arXiv preprint arXiv:2210.04157, 2022	61	2022
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	58	2020

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