| 2025 | COLT | The Space Complexity of Learning-Unlearning Algorithms (extended abstract). | Yeshwanth Cherapanamjeri, Sumegba Garg, Nived Rajaraman, Ayush Sekhari, Abhishek Shetty |
| 2025 | COLT | The Role of Environment Access in Agnostic Reinforcement Learning (Extended Abstract). | Akshay Krishnamurthy, Gene Li, Ayush Sekhari |
| 2025 | ICLR | Machine Unlearning Fails to Remove Data Poisoning Attacks. | Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel |
| 2025 | ICLR | Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics. | Runzhe Wu, Ayush Sekhari, Akshay Krishnamurthy, Wen Sun |
| 2025 | ICML | GaussMark: A Practical Approach for Structural Watermarking of Language Models. | Adam Block, Alexander Rakhlin, Ayush Sekhari |
| 2025 | ICML | System-Aware Unlearning Algorithms: Use Lesser, Forget Faster. | Linda Lu, Ayush Sekhari, Karthik Sridharan |
| 2024 | COLT | Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data. | Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Chen-Yu Wei |
| 2024 | ICLR | Harnessing Density Ratios for Online Reinforcement Learning. | Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie |
| 2024 | ICLR | Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees. | Yifei Zhou, Ayush Sekhari, Yuda Song, Wen Sun |
| 2024 | ICML | Random Latent Exploration for Deep Reinforcement Learning. | Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal |
| 2023 | COLT | Ticketed Learning-Unlearning Schemes. | Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang |
| 2023 | ICLR | Hybrid RL: Using both offline and online data can make RL efficient. | Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, Wen Sun |
| 2023 | ICML | Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. | Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun |
| 2022 | ICML | Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. | Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan |
| 2020 | COLT | Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. | Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan |
| 2019 | COLT | The Complexity of Making the Gradient Small in Stochastic Convex Optimization. | Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth |