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← Authors

Ayush Sekhari

30 papers · 2018–2025 · 4 conferences · across top CS/AI conferences

Achievements

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+12 more ↓ 🌈 Renaissance Researcher (7) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (7) 🌍 Conference Polyglot (4) πŸ—ΊοΈ Taxonomy Completionist (29)
πŸ—ΊοΈ Taxonomy Completionist (29) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🀝 Dynamic Duo (12) πŸ”¬ Deep Specialist (12) πŸ‘‘ Triple Crown πŸ’Ž Century Club (30) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (89) πŸ”₯ Unstoppable (8) ❓ The Questioner ⚑ Prolific Year (6)

Conferences

NIPS (14) COLT (6) ICLR (5) ICML (5)

Top co-authors

Karthik Sridharan (12) Wen Sun (7) Alexander Rakhlin (6) Dylan J Foster (4) Runzhe Wu (3) Gautam Kamath (3) Christoph Dann (3) Akshay Krishnamurthy (3) Mehryar Mohri (3) Yishay Mansour (3)

Research topics

Privacy (1)

Keywords

sample complexity (6) reinforcement learning (5) regret bound (5) policy learning (3) machine unlearning (3) function approximation (3) active learning (2) stochastic gradient descent (2) non-convex optimization (2) imitation learning (2) stationary point (2) oracle complexity (2) partially observable markov decision process (2) agnostic learning (2) stochastic convex optimization (2) convergence analysis (1) convex optimization (1) offline reinforcement learning (1) recursive regularization (1) data poisoning (1)

Papers

Machine Unlearning Fails to Remove Data Poisoning Attacks ICLR 2025 Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics ICLR 2025 System-Aware Unlearning Algorithms: Use Lesser, Forget Faster ICML 2025 GaussMark: A Practical Approach for Structural Watermarking of Language Models ICML 2025 The Role of Environment Access in Agnostic Reinforcement Learning (Extended Abstract) COLT 2025 The Space Complexity of Learning-Unlearning Algorithms (extended abstract) COLT 2025 Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees ICLR 2024 Harnessing Density Ratios for Online Reinforcement Learning ICLR 2024 Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data COLT 2024 Random Latent Exploration for Deep Reinforcement Learning ICML 2024 Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings ICML 2023 Contextual Bandits and Imitation Learning with Preference-Based Active Queries NIPS 2023 Model-Free Reinforcement Learning with the Decision-Estimation Coefficient NIPS 2023 When is Agnostic Reinforcement Learning Statistically Tractable? NIPS 2023 Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks NIPS 2023 Selective Sampling and Imitation Learning via Online Regression NIPS 2023 Ticketed Learning–Unlearning Schemes COLT 2023 Hybrid RL: Using both offline and online data can make RL efficient ICLR 2023 From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent NIPS 2022 Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems NIPS 2022 Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation ICML 2022 On the Complexity of Adversarial Decision Making NIPS 2022 Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations NIPS 2021 Remember What You Want to Forget: Algorithms for Machine Unlearning NIPS 2021 Neural Active Learning with Performance Guarantees NIPS 2021 SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs NIPS 2021 Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations COLT 2020 Reinforcement Learning with Feedback Graphs NIPS 2020 The Complexity of Making the Gradient Small in Stochastic Convex Optimization COLT 2019 Uniform Convergence of Gradients for Non-Convex Learning and Optimization NIPS 2018