Artykuły udostępnione publicznie: - Ayush SekhariWięcej informacji
Dostępne w jakimś miejscu: 17
Remember what you want to forget: Algorithms for machine unlearning
A Sekhari, J Acharya, G Kamath, AT Suresh
Advances in Neural Information Processing Systems 34, 18075-18086, 2021
Upoważnienia: US National Science Foundation, Natural Sciences and Engineering Research …
Uniform convergence of gradients for non-convex learning and optimization
DJ Foster, A Sekhari, K Sridharan
Advances in neural information processing systems 31, 2018
Upoważnienia: US National Science Foundation, US Department of Defense
Guarantees for epsilon-greedy reinforcement learning with function approximation
C Dann, Y Mansour, M Mohri, A Sekhari, K Sridharan
International conference on machine learning, 4666-4689, 2022
Upoważnienia: US National Science Foundation, European Commission
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
Upoważnienia: US National Science Foundation, US Department of Defense
The complexity of making the gradient small in stochastic convex optimization
DJ Foster, A Sekhari, O Shamir, N Srebro, K Sridharan, B Woodworth
Conference on Learning Theory, 1319-1345, 2019
Upoważnienia: US National Science Foundation, European Commission
Sgd: The role of implicit regularization, batch-size and multiple-epochs
A Sekhari, K Sridharan, S Kale
Advances In Neural Information Processing Systems 34, 27422-27433, 2021
Upoważnienia: US National Science Foundation
Provably efficient reinforcement learning in partially observable dynamical systems
M Uehara, A Sekhari, JD Lee, N Kallus, W Sun
Advances in Neural Information Processing Systems 35, 578-592, 2022
Upoważnienia: US National Science Foundation
On the complexity of adversarial decision making
DJ Foster, A Rakhlin, A Sekhari, K Sridharan
Advances in Neural Information Processing Systems 35, 35404-35417, 2022
Upoważnienia: US National Science Foundation, US Department of Energy, US Department of …
Contextual bandits and imitation learning with preference-based active queries
A Sekhari, K Sridharan, W Sun, R Wu
Advances in Neural Information Processing Systems 36, 11261-11295, 2023
Upoważnienia: US National Science Foundation, US Department of Energy
Agnostic reinforcement learning with low-rank MDPs and rich observations
A Sekhari, C Dann, M Mohri, Y Mansour, K Sridharan
Advances in Neural Information Processing Systems 34, 19033-19045, 2021
Upoważnienia: US National Science Foundation, Natural Sciences and Engineering Research …
Computationally efficient pac rl in pomdps with latent determinism and conditional embeddings
M Uehara, A Sekhari, JD Lee, N Kallus, W Sun
International Conference on Machine Learning, 34615-34641, 2023
Upoważnienia: US National Science Foundation
Model-free reinforcement learning with the decision-estimation coefficient
DJ Foster, N Golowich, J Qian, A Rakhlin, A Sekhari
Advances in Neural Information Processing Systems 36, 20080-20117, 2023
Upoważnienia: US National Science Foundation, US Department of Energy, US Department of …
Reinforcement learning with feedback graphs
C Dann, Y Mansour, M Mohri, A Sekhari, K Sridharan
Advances in Neural Information Processing Systems 33, 16868-16878, 2020
Upoważnienia: US National Science Foundation, European Commission
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent
S Kale, JD Lee, C De Sa, A Sekhari, K Sridharan
arXiv preprint arXiv:2210.06705, 2022
Upoważnienia: US National Science Foundation, US Department of Defense
Selective sampling and imitation learning via online regression
A Sekhari, K Sridharan, W Sun, R Wu
Advances in Neural Information Processing Systems 36, 67213-67268, 2023
Upoważnienia: US National Science Foundation, US Department of Energy
Ticketed learning–unlearning schemes
B Ghazi, P Kamath, R Kumar, P Manurangsi, A Sekhari, C Zhang
The Thirty Sixth Annual Conference on Learning Theory, 5110-5139, 2023
Upoważnienia: US National Science Foundation, US Department of Energy
When is agnostic reinforcement learning statistically tractable?
Z Jia, G Li, A Rakhlin, A Sekhari, N Srebro
Advances in Neural Information Processing Systems 36, 27820-27879, 2023
Upoważnienia: US National Science Foundation, US Department of Energy, US Department of …
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