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2020 – today
- 2022
[i13]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. CoRR abs/2206.09421 (2022)- [i12]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. CoRR abs/2206.12020 (2022) - [i11]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. CoRR abs/2206.12081 (2022) - [i10]Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan:
On the Complexity of Adversarial Decision Making. CoRR abs/2206.13063 (2022) - 2021
- [c8]Zhilei Wang, Pranjal Awasthi, Christoph Dann, Ayush Sekhari, Claudio Gentile:
Neural Active Learning with Performance Guarantees. NeurIPS 2021: 7510-7521 - [c7]Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh:
Remember What You Want to Forget: Algorithms for Machine Unlearning. NeurIPS 2021: 18075-18086 - [c6]Ayush Sekhari, Christoph Dann, Mehryar Mohri, Yishay Mansour, Karthik Sridharan:
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. NeurIPS 2021: 19033-19045 - [c5]Ayush Sekhari, Karthik Sridharan, Satyen Kale:
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs. NeurIPS 2021: 27422-27433 - [i9]Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh:
Remember What You Want to Forget: Algorithms for Machine Unlearning. CoRR abs/2103.03279 (2021) - [i8]Pranjal Awasthi, Christoph Dann, Claudio Gentile, Ayush Sekhari, Zhilei Wang:
Neural Active Learning with Performance Guarantees. CoRR abs/2106.03243 (2021) - [i7]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. CoRR abs/2106.11519 (2021) - [i6]Satyen Kale, Ayush Sekhari, Karthik Sridharan:
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs. CoRR abs/2107.05074 (2021) - 2020
- [c4]Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan:
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. COLT 2020: 242-299 - [c3]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Reinforcement Learning with Feedback Graphs. NeurIPS 2020 - [i5]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Reinforcement Learning with Feedback Graphs. CoRR abs/2005.03789 (2020) - [i4]Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan:
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. CoRR abs/2006.13476 (2020)
2010 – 2019
- 2019
- [c2]Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth:
The Complexity of Making the Gradient Small in Stochastic Convex Optimization. COLT 2019: 1319-1345 - [i3]Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth:
The Complexity of Making the Gradient Small in Stochastic Convex Optimization. CoRR abs/1902.04686 (2019) - 2018
- [c1]Dylan J. Foster, Ayush Sekhari, Karthik Sridharan:
Uniform Convergence of Gradients for Non-Convex Learning and Optimization. NeurIPS 2018: 8759-8770 - [i2]Dylan J. Foster, Ayush Sekhari, Karthik Sridharan:
Uniform Convergence of Gradients for Non-Convex Learning and Optimization. CoRR abs/1810.11059 (2018) - 2017
- [i1]Marc Pickett, Ayush Sekhari, James Davidson:
A Brief Study of In-Domain Transfer and Learning from Fewer Samples using A Few Simple Priors. CoRR abs/1707.03979 (2017)
Coauthor Index
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