Dylan J. Foster

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Alexander RakhlinProfessor, MITEmail yang diverifikasi di mit.edu
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteEmail yang diverifikasi di ttic.edu
Akshay KrishnamurthyUniversity of Massachusetts AmherstEmail yang diverifikasi di cs.umass.edu
Ayush SekhariPostdoctoral Associate, MITEmail yang diverifikasi di mit.edu
Haipeng LuoAssociate Professor, University of Southern CaliforniaEmail yang diverifikasi di usc.edu
Noah GolowichMITEmail yang diverifikasi di mit.edu
Max SimchowitzMITEmail yang diverifikasi di andrew.cmu.edu
Nathan SrebroProfessor, TTIC and University of ChicagoEmail yang diverifikasi di ttic.edu
John LangfordMicrosoft Research New YorkEmail yang diverifikasi di hunch.net
Jian QianMITEmail yang diverifikasi di mit.edu
Mehryar MohriHead, ML Theory, Google Research; Professor, Courant Institute of Mathematical Sciences.Email yang diverifikasi di google.com
Tengyang XieAssistant Professor of Computer Science, University of Wisconsin-MadisonEmail yang diverifikasi di cs.wisc.edu
Dipendra MisraStaff Research Scientist, Mosaic Team, DatabricksEmail yang diverifikasi di databricks.com
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyEmail yang diverifikasi di cs.berkeley.edu
Matus TelgarskyCourant Institute of Mathematical Sciences, New York UniversityEmail yang diverifikasi di nyu.edu
Sham M KakadeHarvard UniversityEmail yang diverifikasi di seas.harvard.edu
Yair CarmonTel Aviv UniversityEmail yang diverifikasi di tauex.tau.ac.il
John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford UniversityEmail yang diverifikasi di stanford.edu
Blake WoodworthGeorge Washington UniversityEmail yang diverifikasi di gwu.edu
Zakaria MhammediGoogle ResearchEmail yang diverifikasi di mit.edu

Ikuti

Dylan J. Foster

Principal Researcher, Microsoft Research

Email yang diverifikasi di microsoft.com - Beranda

Machine Learning Optimization Statistics Reinforcement Learning Deep Learning


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems 30, 2017	1413	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	379	2023
Orthogonal statistical learning DJ Foster, V Syrgkanis The Annals of Statistics 51 (3), 879-908, 2023	273	2023
Beyond ucb: Optimal and efficient contextual bandits with regression oracles D Foster, A Rakhlin International Conference on Machine Learning, 3199-3210, 2020	236	2020
Naive exploration is optimal for online lqr M Simchowitz, D Foster International Conference on Machine Learning, 8937-8948, 2020	229	2020
The statistical complexity of interactive decision making DJ Foster, SM Kakade, J Qian, A Rakhlin arXiv preprint arXiv:2112.13487, 2021	207	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	204	2020
Practical contextual bandits with regression oracles D Foster, A Agarwal, M Dudík, H Luo, R Schapire International Conference on Machine Learning, 1539-1548, 2018	142	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	134	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	116	2020
Model selection for contextual bandits DJ Foster, A Krishnamurthy, H Luo Advances in Neural Information Processing Systems 32, 2019	107	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	102	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	101	2020
Logarithmic regret for adversarial online control D Foster, M Simchowitz International Conference on Machine Learning, 3211-3221, 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	87	2018
Learning nonlinear dynamical systems from a single trajectory D Foster, T Sarkar, A Rakhlin Learning for Dynamics and Control, 851-861, 2020	82	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	78	2022
Parameter-free online learning via model selection DJ Foster, S Kale, M Mohri, K Sridharan Advances in Neural Information Processing Systems 30, 2017	73	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	67	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	62	2020

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