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ORCIDAyush Sekhari
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2020 – today
- 2024
[c24]Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Chen-Yu Wei:
Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data. COLT 2024: 2644-2719- [c23]Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie:
Harnessing Density Ratios for Online Reinforcement Learning. ICLR 2024 - [c22]Yifei Zhou, Ayush Sekhari, Yuda Song, Wen Sun:
Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees. ICLR 2024 - [c21]Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal:
Random Latent Exploration for Deep Reinforcement Learning. ICML 2024 - [i28]Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie:
Harnessing Density Ratios for Online Reinforcement Learning. CoRR abs/2401.09681 (2024) - [i27]Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Chen-Yu Wei:
Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data. CoRR abs/2403.17091 (2024) - [i26]Runzhe Wu, Ayush Sekhari, Akshay Krishnamurthy, Wen Sun:
Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics. CoRR abs/2406.11810 (2024) - [i25]Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel:
Machine Unlearning Fails to Remove Data Poisoning Attacks. CoRR abs/2406.17216 (2024) - [i24]August Y. Chen, Ayush Sekhari, Karthik Sridharan:
Langevin Dynamics: A Unified Perspective on Optimization via Lyapunov Potentials. CoRR abs/2407.04264 (2024) - [i23]Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal:
Random Latent Exploration for Deep Reinforcement Learning. CoRR abs/2407.13755 (2024) - 2023
- [c20]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang:
Ticketed Learning-Unlearning Schemes. COLT 2023: 5110-5139 - [c19]Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, Wen Sun:
Hybrid RL: Using both offline and online data can make RL efficient. ICLR 2023 - [c18]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. ICML 2023: 34615-34641 - [c17]Jimmy Z. Di, Jack Douglas, Jayadev Acharya, Gautam Kamath, Ayush Sekhari:
Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks. NeurIPS 2023 - [c16]Dylan J. Foster, Noah Golowich, Jian Qian, Alexander Rakhlin, Ayush Sekhari:
Model-Free Reinforcement Learning with the Decision-Estimation Coefficient. NeurIPS 2023 - [c15]Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nati Srebro:
When is Agnostic Reinforcement Learning Statistically Tractable? NeurIPS 2023 - [c14]Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu:
Contextual Bandits and Imitation Learning with Preference-Based Active Queries. NeurIPS 2023 - [c13]Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu:
Selective Sampling and Imitation Learning via Online Regression. NeurIPS 2023 - [i22]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang:
Ticketed Learning-Unlearning Schemes. CoRR abs/2306.15744 (2023) - [i21]Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu:
Selective Sampling and Imitation Learning via Online Regression. CoRR abs/2307.04998 (2023) - [i20]Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu:
Contextual Bandits and Imitation Learning via Preference-Based Active Queries. CoRR abs/2307.12926 (2023) - [i19]Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nathan Srebro:
When is Agnostic Reinforcement Learning Statistically Tractable? CoRR abs/2310.06113 (2023) - [i18]Yifei Zhou, Ayush Sekhari, Yuda Song, Wen Sun:
Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees. CoRR abs/2311.08384 (2023) - 2022
- [c12]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. ICML 2022: 4666-4689 - [c11]Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan:
On the Complexity of Adversarial Decision Making. NeurIPS 2022 - [c10]Christopher De Sa, Satyen Kale, Jason D. Lee, Ayush Sekhari, Karthik Sridharan:
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. NeurIPS 2022 - [c9]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. NeurIPS 2022 - [i17]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. CoRR abs/2206.09421 (2022) - [i16]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) - [i15]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) - [i14]Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan:
On the Complexity of Adversarial Decision Making. CoRR abs/2206.13063 (2022) - [i13]Satyen Kale, Jason D. Lee, Chris De Sa, Ayush Sekhari, Karthik Sridharan:
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. CoRR abs/2210.06705 (2022) - [i12]Yuda Song, Yifei Zhou, Ayush Sekhari, J. Andrew Bagnell, Akshay Krishnamurthy, Wen Sun:
Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient. CoRR abs/2210.06718 (2022) - [i11]Dylan J. Foster, Noah Golowich, Jian Qian, Alexander Rakhlin, Ayush Sekhari:
A Note on Model-Free Reinforcement Learning with the Decision-Estimation Coefficient. CoRR abs/2211.14250 (2022) - [i10]Jimmy Z. Di, Jack Douglas, Jayadev Acharya, Gautam Kamath
, Ayush Sekhari:
Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks. CoRR abs/2212.10717 (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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last updated on 2024-09-04 01:23 CEST by the dblp team
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