ML Anthology

Sekhari, Ayush

40 publications

ICLR 2025 Computationally Efficient RL Under Linear Bellman Completeness for Deterministic Dynamics Runzhe Wu, Ayush Sekhari, Akshay Krishnamurthy, Wen Sun
ICML 2025 GaussMark: A Practical Approach for Structural Watermarking of Language Models Adam Block, Alexander Rakhlin, Ayush Sekhari
ICLR 2025 Machine Unlearning Fails to Remove Data Poisoning Attacks Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel
ICML 2025 System-Aware Unlearning Algorithms: Use Lesser, Forget Faster Linda Lu, Ayush Sekhari, Karthik Sridharan
NeurIPS 2025 The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches Omri Lev, Vishwak Srinivasan, Moshe Shenfeld, Katrina Ligett, Ayush Sekhari, Ashia C. Wilson
COLT 2025 The Role of Environment Access in Agnostic Reinforcement Learning (Extended Abstract) Akshay Krishnamurthy, Gene Li, Ayush Sekhari
COLT 2025 The Space Complexity of Learning-Unlearning Algorithms (extended Abstract) Yeshwanth Cherapanamjeri, Sumegba Garg, Nived Rajaraman, Ayush Sekhari, Abhishek Shetty
ICLRW 2025 Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models Vinith Menon Suriyakumar, Rohan Alur, Ayush Sekhari, Manish Raghavan, Ashia C. Wilson
ICLR 2024 Harnessing Density Ratios for Online Reinforcement Learning Philip Amortila, Dylan J Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie
NeurIPSW 2024 Langevin Dynamics: A Unified Perspective on Optimization via Lyapunov Potentials August Y Chen, Ayush Sekhari, Karthik Sridharan
ICLR 2024 Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees Yifei Zhou, Ayush Sekhari, Yuda Song, Wen Sun
COLT 2024 Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Chen-Yu Wei
ICML 2024 Random Latent Exploration for Deep Reinforcement Learning Srinath V. Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal
ICML 2023 Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
NeurIPS 2023 Contextual Bandits and Imitation Learning with Preference-Based Active Queries Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
ICMLW 2023 Contextual Bandits and Imitation Learning with Preference-Based Active Queries Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
ICMLW 2023 Contextual Bandits and Imitation Learning with Preference-Based Active Queries Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
NeurIPS 2023 Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks Jimmy Di, Jack Douglas, Jayadev Acharya, Gautam Kamath, Ayush Sekhari
ICLR 2023 Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, Wen Sun
NeurIPS 2023 Model-Free Reinforcement Learning with the Decision-Estimation Coefficient Dylan J Foster, Noah Golowich, Jian Qian, Alexander Rakhlin, Ayush Sekhari
NeurIPS 2023 Selective Sampling and Imitation Learning via Online Regression Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
ICMLW 2023 Selective Sampling and Imitation Learning via Online Regression Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
COLT 2023 Ticketed Learning–Unlearning Schemes Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang
NeurIPS 2023 When Is Agnostic Reinforcement Learning Statistically Tractable? Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nati Srebro
ICMLW 2023 When Is Agnostic Reinforcement Learning Statistically Tractable? Gene Li, Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Nathan Srebro
NeurIPS 2022 From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent Christopher M De Sa, Satyen Kale, Jason Lee, Ayush Sekhari, Karthik Sridharan
ICML 2022 Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation Chris Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
NeurIPSW 2022 Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks Jimmy Z. Di, Jack Douglas, Jayadev Acharya, Gautam Kamath, Ayush Sekhari
NeurIPSW 2022 Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks Jimmy Z. Di, Jack Douglas, Jayadev Acharya, Gautam Kamath, Ayush Sekhari
NeurIPSW 2022 Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, Wen Sun
NeurIPS 2022 On the Complexity of Adversarial Decision Making Dylan J Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan
NeurIPS 2022 Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems Masatoshi Uehara, Ayush Sekhari, Jason Lee, Nathan Kallus, Wen Sun
NeurIPS 2021 Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations Ayush Sekhari, Christoph Dann, Mehryar Mohri, Yishay Mansour, Karthik Sridharan
NeurIPS 2021 Neural Active Learning with Performance Guarantees Zhilei Wang, Pranjal Awasthi, Christoph Dann, Ayush Sekhari, Claudio Gentile
NeurIPS 2021 Remember What You Want to Forget: Algorithms for Machine Unlearning Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh
NeurIPS 2021 SGD: The Role of Implicit Regularization, Batch-Size and Multiple-Epochs Ayush Sekhari, Karthik Sridharan, Satyen Kale
NeurIPS 2020 Reinforcement Learning with Feedback Graphs Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
COLT 2020 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
COLT 2019 The Complexity of Making the Gradient Small in Stochastic Convex Optimization Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake Woodworth
NeurIPS 2018 Uniform Convergence of Gradients for Non-Convex Learning and Optimization Dylan J Foster, Ayush Sekhari, Karthik Sridharan