Ayush Sekhari - RankMe

65 papers A* 30Journal 35

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics. ICLR Runzhe Wu, Ayush Sekhari, Akshay Krishnamurthy, Wen Sun
2025	A*	`conf`	GaussMark: A Practical Approach for Structural Watermarking of Language Models. ICML Adam Block, Alexander Rakhlin, Ayush Sekhari
2025	J	`jnl`	GaussMark: A Practical Approach for Structural Watermarking of Language Models. CoRR Adam Block, Ayush Sekhari, Alexander Rakhlin
2025	A*	`conf`	Machine Unlearning Fails to Remove Data Poisoning Attacks. ICLR Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel
2025	A*	`conf`	System-Aware Unlearning Algorithms: Use Lesser, Forget Faster. ICML Linda Lu, Ayush Sekhari, Karthik Sridharan
2025	J	`jnl`	System-Aware Unlearning Algorithms: Use Lesser, Forget Faster. CoRR Linda Lu, Ayush Sekhari, Karthik Sridharan
2025	J	`jnl`	The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches. CoRR Omri Lev, Vishwak Srinivasan, Moshe Shenfeld, Katrina Ligett, Ayush Sekhari, Ashia C. Wilson
2025	A*	`conf`	The Role of Environment Access in Agnostic Reinforcement Learning (Extended Abstract). COLT Akshay Krishnamurthy, Gene Li, Ayush Sekhari
2025	J	`jnl`	The Role of Environment Access in Agnostic Reinforcement Learning. CoRR Akshay Krishnamurthy, Gene Li, Ayush Sekhari
2025	A*	`conf`	The Space Complexity of Learning-Unlearning Algorithms (extended abstract). COLT Yeshwanth Cherapanamjeri, Sumegba Garg, Nived Rajaraman, Ayush Sekhari, Abhishek Shetty
2025	J	`jnl`	The Space Complexity of Learning-Unlearning Algorithms. CoRR Yeshwanth Cherapanamjeri, Sumegha Garg, Nived Rajaraman, Ayush Sekhari, Abhishek Shetty
2025	J	`jnl`	UCD: Unlearning in LLMs via Contrastive Decoding. CoRR Vinith M. Suriyakumar, Ayush Sekhari, Ashia Wilson
2024	J	`jnl`	Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics. CoRR Runzhe Wu, Ayush Sekhari, Akshay Krishnamurthy, Wen Sun
2024	A*	`conf`	Harnessing Density Ratios for Online Reinforcement Learning. ICLR Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie
2024	J	`jnl`	Harnessing Density Ratios for Online Reinforcement Learning. CoRR Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie
2024	J	`jnl`	Langevin Dynamics: A Unified Perspective on Optimization via Lyapunov Potentials. CoRR August Y. Chen, Ayush Sekhari, Karthik Sridharan
2024	J	`jnl`	Machine Unlearning Fails to Remove Data Poisoning Attacks. CoRR Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel
2024	A*	`conf`	Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees. ICLR Yifei Zhou, Ayush Sekhari, Yuda Song, Wen Sun
2024	A*	`conf`	Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data. COLT Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Chen-Yu Wei
2024	J	`jnl`	Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data. CoRR Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Chen-Yu Wei
2024	A*	`conf`	Random Latent Exploration for Deep Reinforcement Learning. ICML Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal
2024	J	`jnl`	Random Latent Exploration for Deep Reinforcement Learning. CoRR Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal
2024	J	`jnl`	Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models. CoRR Vinith M. Suriyakumar, Rohan Alur, Ayush Sekhari, Manish Raghavan, Ashia C. Wilson
2023	A*	`conf`	Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. ICML Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
2023	J	`jnl`	Contextual Bandits and Imitation Learning via Preference-Based Active Queries. CoRR Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
2023	A*	`conf`	Contextual Bandits and Imitation Learning with Preference-Based Active Queries. NeurIPS Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
2023	A*	`conf`	Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks. NeurIPS Jimmy Z. Di, Jack Douglas, Jayadev Acharya, Gautam Kamath, Ayush Sekhari
2023	A*	`conf`	Hybrid RL: Using both offline and online data can make RL efficient. ICLR Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, Wen Sun
2023	A*	`conf`	Model-Free Reinforcement Learning with the Decision-Estimation Coefficient. NeurIPS Dylan J. Foster, Noah Golowich, Jian Qian, Alexander Rakhlin, Ayush Sekhari
2023	J	`jnl`	Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees. CoRR Yifei Zhou, Ayush Sekhari, Yuda Song, Wen Sun
2023	A*	`conf`	Selective Sampling and Imitation Learning via Online Regression. NeurIPS Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
2023	J	`jnl`	Selective Sampling and Imitation Learning via Online Regression. CoRR Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
2023	A*	`conf`	Ticketed Learning-Unlearning Schemes. COLT Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang
2023	J	`jnl`	Ticketed Learning-Unlearning Schemes. CoRR Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang
2023	A*	`conf`	When is Agnostic Reinforcement Learning Statistically Tractable? NeurIPS Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nati Srebro
2023	J	`jnl`	When is Agnostic Reinforcement Learning Statistically Tractable? CoRR Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nathan Srebro
2022	J	`jnl`	A Note on Model-Free Reinforcement Learning with the Decision-Estimation Coefficient. CoRR Dylan J. Foster, Noah Golowich, Jian Qian, Alexander Rakhlin, Ayush Sekhari
2022	J	`jnl`	Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. CoRR Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
2022	A*	`conf`	From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. NeurIPS Christopher De Sa, Satyen Kale, Jason D. Lee, Ayush Sekhari, Karthik Sridharan
2022	J	`jnl`	From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. CoRR Satyen Kale, Jason D. Lee, Chris De Sa, Ayush Sekhari, Karthik Sridharan
2022	A*	`conf`	Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. ICML Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
2022	J	`jnl`	Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. CoRR Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
2022	J	`jnl`	Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks. CoRR Jimmy Z. Di, Jack Douglas, Jayadev Acharya, Gautam Kamath, Ayush Sekhari
2022	J	`jnl`	Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient. CoRR Yuda Song, Yifei Zhou, Ayush Sekhari, J. Andrew Bagnell, Akshay Krishnamurthy, Wen Sun
2022	A*	`conf`	On the Complexity of Adversarial Decision Making. NeurIPS Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan
2022	J	`jnl`	On the Complexity of Adversarial Decision Making. CoRR Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan
2022	A*	`conf`	Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. NeurIPS Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
2022	J	`jnl`	Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. CoRR Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
2021	A*	`conf`	Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. NeurIPS Ayush Sekhari, Christoph Dann, Mehryar Mohri, Yishay Mansour, Karthik Sridharan
2021	J	`jnl`	Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. CoRR Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
2021	A*	`conf`	Neural Active Learning with Performance Guarantees. NeurIPS Zhilei Wang, Pranjal Awasthi, Christoph Dann, Ayush Sekhari, Claudio Gentile
2021	J	`jnl`	Neural Active Learning with Performance Guarantees. CoRR Pranjal Awasthi, Christoph Dann, Claudio Gentile, Ayush Sekhari, Zhilei Wang
2021	A*	`conf`	Remember What You Want to Forget: Algorithms for Machine Unlearning. NeurIPS Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh
2021	J	`jnl`	Remember What You Want to Forget: Algorithms for Machine Unlearning. CoRR Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh
2021	A*	`conf`	SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs. NeurIPS Ayush Sekhari, Karthik Sridharan, Satyen Kale
2021	J	`jnl`	SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs. CoRR Satyen Kale, Ayush Sekhari, Karthik Sridharan
2020	A*	`conf`	Reinforcement Learning with Feedback Graphs. NeurIPS Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
2020	J	`jnl`	Reinforcement Learning with Feedback Graphs. CoRR Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
2020	A*	`conf`	Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. COLT Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
2020	J	`jnl`	Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. CoRR Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
2019	A*	`conf`	The Complexity of Making the Gradient Small in Stochastic Convex Optimization. COLT Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth
2019	J	`jnl`	The Complexity of Making the Gradient Small in Stochastic Convex Optimization. CoRR Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth
2018	A*	`conf`	Uniform Convergence of Gradients for Non-Convex Learning and Optimization. NeurIPS Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
2018	J	`jnl`	Uniform Convergence of Gradients for Non-Convex Learning and Optimization. CoRR Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
2017	J	`jnl`	A Brief Study of In-Domain Transfer and Learning from Fewer Samples using A Few Simple Priors. CoRR Marc Pickett, Ayush Sekhari, James Davidson