A Brief Study of In-Domain Transfer and Learning from Fewer Samples using A Few Simple Priors. - Marc Pickett; Ayush Sekhari; James Davidson (CoRR, 2017)

Uniform Convergence of Gradients for Non-Convex Learning and Optimization. - Dylan J. Foster; Ayush Sekhari; Karthik Sridharan (NeurIPS, 2018)

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 (COLT, 2019)

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, 2020)

Reinforcement Learning with Feedback Graphs. - Christoph Dann; Yishay Mansour; Mehryar Mohri; Ayush Sekhari; Karthik Sridharan (NeurIPS, 2020)

Remember What You Want to Forget: Algorithms for Machine Unlearning. - Ayush Sekhari; Jayadev Acharya; Gautam Kamath 001; Ananda Theertha Suresh (NeurIPS, 2021)

Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. - Ayush Sekhari; Christoph Dann; Mehryar Mohri; Yishay Mansour; Karthik Sridharan (NeurIPS, 2021)

SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs. - Ayush Sekhari; Karthik Sridharan; Satyen Kale (NeurIPS, 2021)

Neural Active Learning with Performance Guarantees. - Zhilei Wang; Pranjal Awasthi; Christoph Dann; Ayush Sekhari; Claudio Gentile (NeurIPS, 2021)

Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. - Christoph Dann; Yishay Mansour; Mehryar Mohri; Ayush Sekhari; Karthik Sridharan (ICML, 2022)

On the Complexity of Adversarial Decision Making. - Dylan J. Foster; Alexander Rakhlin; Ayush Sekhari; Karthik Sridharan (NeurIPS, 2022)

From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. - Christopher De Sa; Satyen Kale; Jason D. Lee; Ayush Sekhari; Karthik Sridharan (NeurIPS, 2022)

Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. - Masatoshi Uehara; Ayush Sekhari; Jason D. Lee; Nathan Kallus; Wen Sun 002 (NeurIPS, 2022)

A Note on Model-Free Reinforcement Learning with the Decision-Estimation Coefficient. - Dylan J. Foster; Noah Golowich; Jian Qian; Alexander Rakhlin; Ayush Sekhari (CoRR, 2022)

Ticketed Learning-Unlearning Schemes. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Ayush Sekhari; Chiyuan Zhang (COLT, 2023)

Hybrid RL: Using both offline and online data can make RL efficient. - Yuda Song 001; Yifei Zhou; Ayush Sekhari; Drew Bagnell; Akshay Krishnamurthy; Wen Sun 002 (ICLR, 2023)

Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. - Masatoshi Uehara; Ayush Sekhari; Jason D. Lee; Nathan Kallus; Wen Sun 002 (ICML, 2023)

Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks. - Jimmy Z. Di; Jack Douglas; Jayadev Acharya; Gautam Kamath 001; Ayush Sekhari (NeurIPS, 2023)

Model-Free Reinforcement Learning with the Decision-Estimation Coefficient. - Dylan J. Foster; Noah Golowich; Jian Qian; Alexander Rakhlin; Ayush Sekhari (NeurIPS, 2023)

When is Agnostic Reinforcement Learning Statistically Tractable? - Zeyu Jia; Gene Li; Alexander Rakhlin; Ayush Sekhari; Nati Srebro (NeurIPS, 2023)

Contextual Bandits and Imitation Learning with Preference-Based Active Queries. - Ayush Sekhari; Karthik Sridharan; Wen Sun 002; Runzhe Wu (NeurIPS, 2023)

Selective Sampling and Imitation Learning via Online Regression. - Ayush Sekhari; Karthik Sridharan; Wen Sun 002; Runzhe Wu (NeurIPS, 2023)

Contextual Bandits and Imitation Learning via Preference-Based Active Queries. - Ayush Sekhari; Karthik Sridharan; Wen Sun 002; Runzhe Wu (CoRR, 2023)

Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data. - Zeyu Jia; Alexander Rakhlin; Ayush Sekhari; Chen-Yu Wei (COLT, 2024)

Harnessing Density Ratios for Online Reinforcement Learning. - Philip Amortila; Dylan J. Foster; Nan Jiang 008; Ayush Sekhari; Tengyang Xie (ICLR, 2024)

Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees. - Yifei Zhou; Ayush Sekhari; Yuda Song 001; Wen Sun 002 (ICLR, 2024)

Random Latent Exploration for Deep Reinforcement Learning. - Srinath Mahankali; Zhang-Wei Hong; Ayush Sekhari; Alexander Rakhlin; Pulkit Agrawal 001 (ICML, 2024)

Langevin Dynamics: A Unified Perspective on Optimization via Lyapunov Potentials. - August Y. Chen; Ayush Sekhari; Karthik Sridharan (CoRR, 2024)

Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models. - Vinith M. Suriyakumar; Rohan Alur; Ayush Sekhari; Manish Raghavan; Ashia C. Wilson (CoRR, 2024)

The Space Complexity of Learning-Unlearning Algorithms (extended abstract). - Yeshwanth Cherapanamjeri; Sumegba Garg; Nived Rajaraman; Ayush Sekhari; Abhishek Shetty (COLT, 2025)

The Role of Environment Access in Agnostic Reinforcement Learning (Extended Abstract). - Akshay Krishnamurthy; Gene Li; Ayush Sekhari (COLT, 2025)

Machine Unlearning Fails to Remove Data Poisoning Attacks. - Martin Pawelczyk; Jimmy Z. Di; Yiwei Lu 001; Gautam Kamath 001; Ayush Sekhari; Seth Neel (ICLR, 2025)

Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics. - Runzhe Wu; Ayush Sekhari; Akshay Krishnamurthy; Wen Sun 002 (ICLR, 2025)

GaussMark: A Practical Approach for Structural Watermarking of Language Models. - Adam Block; Ayush Sekhari; Alexander Rakhlin (CoRR, 2025)

The Role of Environment Access in Agnostic Reinforcement Learning. - Akshay Krishnamurthy; Gene Li; Ayush Sekhari (CoRR, 2025)

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches. - Omri Lev; Vishwak Srinivasan; Moshe Shenfeld; Katrina Ligett; Ayush Sekhari; Ashia C. Wilson (CoRR, 2025)

System-Aware Unlearning Algorithms: Use Lesser, Forget Faster. - Linda Lu; Ayush Sekhari; Karthik Sridharan (CoRR, 2025)

UCD: Unlearning in LLMs via Contrastive Decoding. - Vinith M. Suriyakumar; Ayush Sekhari; Ashia Wilson (CoRR, 2025)

The Space Complexity of Learning-Unlearning Algorithms. - Yeshwanth Cherapanamjeri; Sumegha Garg; Nived Rajaraman; Ayush Sekhari; Abhishek Shetty (CoRR, 2025)