Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems 30, 2017 | 1252 | 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 | 301 | 2023 |
Orthogonal statistical learning DJ Foster, V Syrgkanis The Annals of Statistics 51 (3), 879-908, 2023 | 226 | 2023 |
Beyond ucb: Optimal and efficient contextual bandits with regression oracles D Foster, A Rakhlin International Conference on Machine Learning, 3199-3210, 2020 | 194 | 2020 |
Naive exploration is optimal for online lqr M Simchowitz, D Foster International Conference on Machine Learning, 8937-8948, 2020 | 194 | 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 | 173 | 2020 |
The statistical complexity of interactive decision making DJ Foster, SM Kakade, J Qian, A Rakhlin arXiv preprint arXiv:2112.13487, 2021 | 168 | 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 | 122 | 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 | 122 | 2016 |
Model selection for contextual bandits DJ Foster, A Krishnamurthy, H Luo Advances in Neural Information Processing Systems 32, 2019 | 99 | 2019 |
Adapting to misspecification in contextual bandits DJ Foster, C Gentile, M Mohri, J Zimmert Advances in Neural Information Processing Systems 33, 11478-11489, 2020 | 96 | 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 | 88 | 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 | 81 | 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 | 81 | 2018 |
Logarithmic regret for adversarial online control D Foster, M Simchowitz International Conference on Machine Learning, 3211-3221, 2020 | 73 | 2020 |
Learning nonlinear dynamical systems from a single trajectory D Foster, T Sarkar, A Rakhlin Learning for Dynamics and Control, 851-861, 2020 | 71 | 2020 |
Offline reinforcement learning: Fundamental barriers for value function approximation DJ Foster, A Krishnamurthy, D Simchi-Levi, Y Xu arXiv preprint arXiv:2111.10919, 2021 | 64 | 2021 |
Parameter-free online learning via model selection DJ Foster, S Kale, M Mohri, K Sridharan Advances in Neural Information Processing Systems 30, 2017 | 64 | 2017 |
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 | 56 | 2020 |
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 | 53 | 2019 |