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Dylan J. Foster
Dylan J. Foster
Principal Researcher, Microsoft Research
Verified email at microsoft.com - Homepage
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
12472017
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
2962023
Orthogonal statistical learning
DJ Foster, V Syrgkanis
The Annals of Statistics 51 (3), 879-908, 2023
2222023
Beyond ucb: Optimal and efficient contextual bandits with regression oracles
D Foster, A Rakhlin
International Conference on Machine Learning, 3199-3210, 2020
1942020
Naive exploration is optimal for online lqr
M Simchowitz, D Foster
International Conference on Machine Learning, 8937-8948, 2020
1942020
Independent policy gradient methods for competitive reinforcement learning
C Daskalakis, DJ Foster, N Golowich
Advances in neural information processing systems 33, 5527-5540, 2020
1702020
The statistical complexity of interactive decision making
DJ Foster, SM Kakade, J Qian, A Rakhlin
arXiv preprint arXiv:2112.13487, 2021
1672021
Practical contextual bandits with regression oracles
D Foster, A Agarwal, M Dudík, H Luo, R Schapire
International Conference on Machine Learning, 1539-1548, 2018
1222018
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
1212016
Model selection for contextual bandits
DJ Foster, A Krishnamurthy, H Luo
Advances in Neural Information Processing Systems 32, 2019
972019
Adapting to misspecification in contextual bandits
DJ Foster, C Gentile, M Mohri, J Zimmert
Advances in Neural Information Processing Systems 33, 11478-11489, 2020
962020
Logistic regression: The importance of being improper
DJ Foster, S Kale, H Luo, M Mohri, K Sridharan
Conference on learning theory, 167-208, 2018
882018
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
812020
Uniform convergence of gradients for non-convex learning and optimization
DJ Foster, A Sekhari, K Sridharan
Advances in neural information processing systems 31, 2018
792018
Logarithmic regret for adversarial online control
D Foster, M Simchowitz
International Conference on Machine Learning, 3211-3221, 2020
732020
Learning nonlinear dynamical systems from a single trajectory
D Foster, T Sarkar, A Rakhlin
Learning for Dynamics and Control, 851-861, 2020
712020
Parameter-free online learning via model selection
DJ Foster, S Kale, M Mohri, K Sridharan
Advances in Neural Information Processing Systems 30, 2017
642017
Offline reinforcement learning: Fundamental barriers for value function approximation
DJ Foster, A Krishnamurthy, D Simchi-Levi, Y Xu
arXiv preprint arXiv:2111.10919, 2021
632021
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
562020
The Complexity of Making the Gradient Small in Stochastic Convex Optimization
DJ Foster, A Sekhari, O Shamir, N Srebro, K Sridharan, B Woodworth
arXiv preprint arXiv:1902.04686, 2019
532019
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