Vaneet Aggarwal

ECPv2: Fast, Efficient, and Scalable Global Optimization of Lipschitz Functions AAAI 2026 Efficient Q-Learning and Actor–Critic Methods for Robust Average-Reward Reinforcement Learning. UAI 2026 Global Convergence of Average Reward Constrained MDPs with Neural Critic and General Policy Parameterization UAI 2026 On the Convergence of Self-Improving Online LLM Alignment UAI 2026 Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach ICML 2025 Align-Pro: A Principled Approach to Prompt Optimization for LLM Alignment AAAI 2025 A Sharper Global Convergence Analysis for Average Reward Reinforcement Learning via an Actor-Critic Approach ICML 2025 Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis ICLR 2025 Every Call is Precious: Global Optimization of Black-Box Functions with Unknown Lipschitz Constants AISTATS 2025 Order-Optimal Global Convergence for Actor-Critic with General Policy and Neural Critic Parametrization UAI 2025 Order-Optimal Regret with Novel Policy Gradient Approaches in Infinite-Horizon Average Reward MDPs AISTATS 2025 Quantum Speedups in Regret Analysis of Infinite Horizon Average-Reward Markov Decision Processes ICML 2025 Variational Offline Multi-agent Skill Discovery IJCAI 2025 Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization ICML 2024 Combinatorial Stochastic-Greedy Bandit AAAI 2024 Federated Combinatorial Multi-Agent Multi-Armed Bandits ICML 2024 From Linear to Linearizable Optimization: A Novel Framework with Applications to Stationary and Non-stationary DR-submodular Optimization NeurIPS 2024 Gradient Methods for Online DR-Submodular Maximization with Stochastic Long-Term Constraints NeurIPS 2024 Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model ICLR 2024 Improved Sample Complexity Analysis of Natural Policy Gradient Algorithm with General Parameterization for Infinite Horizon Discounted Reward Markov Decision Processes AISTATS 2024 Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm NeurIPS 2024 Mean-Field Approximation of Cooperative Constrained Multi-Agent Reinforcement Learning (CMARL) JMLR 2024 Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes AAAI 2024 Sample-Efficient Constrained Reinforcement Learning with General Parameterization NeurIPS 2024 Stochastic Q-learning for Large Discrete Action Spaces ICML 2024 Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles ICML 2024 Unified Projection-Free Algorithms for Adversarial DR-Submodular Optimization ICLR 2024 Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Conservative Natural Policy Gradient Primal-Dual Algorithm AAAI 2023 A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback ICML 2023 A Unified Algorithm Framework for Unsupervised Discovery of Skills based on Determinantal Point Process NeurIPS 2023 A Unified Approach for Maximizing Continuous DR-submodular Functions NeurIPS 2023 Domain Adaptive Few-Shot Open-Set Learning ICCV 2023 Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning NeurIPS 2023 Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates NeurIPS 2023 Multi-task Hierarchical Adversarial Inverse Reinforcement Learning ICML 2023 On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization ICML 2023 Provably Sample-Efficient Model-Free Algorithm for MDPs with Peak Constraints JMLR 2023 Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback AISTATS 2023 Reinforcement Learning for Joint Optimization of Multiple Rewards JMLR 2023 Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach AAAI 2022 An explore-then-commit algorithm for submodular maximization under full-bandit feedback UAI 2022 Can mean field control (mfc) approximate cooperative multi agent reinforcement learning (marl) with non-uniform interaction? UAI 2022 FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning ICML 2022 Information theoretic approach to detect collusion in multi-agent games UAI 2022 Multi-Agent Multi-Armed Bandits with Limited Communication JMLR 2022 On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC) JMLR 2022 PAC: Assisted Value Factorization with Counterfactual Predictions in Multi-Agent Reinforcement Learning NeurIPS 2022 Regret guarantees for model-based reinforcement learning with long-term average constraints UAI 2022 Scalable Multi-agent Covering Option Discovery based on Kronecker Graphs NeurIPS 2022 Communication efficient parallel reinforcement learning UAI 2021 DART: Adaptive Accept Reject Algorithm for Non-Linear Combinatorial Bandits AAAI 2021 Reinforcement Learning for Constrained Markov Decision Processes AISTATS 2021 Stochastic Top-K Subset Bandits with Linear Space and Non-Linear Feedback ALT 2021 Efficient Large-Scale Gaussian Process Bandits by Believing only Informative Actions L4DC 2020 GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning JMLR 2020 Wide Compression: Tensor Ring Nets CVPR 2018 Efficient Low Rank Tensor Ring Completion ICCV 2017 Rank Determination for Low-Rank Data Completion JMLR 2017