Srikant, R

26 publications

AAAI 2025 Decentralized and Uncoordinated Learning of Stable Matchings: A Game-Theoretic Approach S. Rasoul Etesami, R. Srikant

ICLR 2025 Global Convergence of Policy Gradient in Average Reward MDPs Navdeep Kumar, Yashaswini Murthy, Itai Shufaro, Kfir Yehuda Levy, R. Srikant, Shie Mannor

ICML 2025 Reinforcement Learning with Segment Feedback Yihan Du, Anna Winnicki, Gal Dalal, Shie Mannor, R. Srikant

NeurIPS 2025 Scalable Policy-Based RL Algorithms for POMDPs Ameya Anjarlekar, S. Rasoul Etesami, R. Srikant

TMLR 2025 Spectral Clustering and Labeling for Crowdsourcing with Inherently Distinct Task Types Saptarshi Mandal, Seo Taek Kong, Dimitrios Katselis, R. Srikant

ICLR 2024 Cascading Reinforcement Learning Yihan Du, R. Srikant, Wei Chen

ICML 2024 Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization Yihan Du, Anna Winnicki, Gal Dalal, Shie Mannor, R. Srikant

TMLR 2024 Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm Semih Cayci, Niao He, R. Srikant

ICML 2023 Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits Ronshee Chawla, Daniel Vial, Sanjay Shakkottai, R. Srikant

AISTATS 2023 Learning While Scheduling in Multi-Server Systems with Unknown Statistics: MaxWeight with Discounted UCB Zixian Yang, R. Srikant, Lei Ying

L4DC 2023 Modified Policy Iteration for Exponential Cost Risk Sensitive MDPs Yashaswini Murthy, Mehrdad Moharrami, R. Srikant

AISTATS 2023 On the Convergence of Policy Iteration-Based Reinforcement Learning with Monte Carlo Policy Evaluation Anna Winnicki, R. Srikant

NeurIPS 2023 Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms Yashaswini Murthy, Mehrdad Moharrami, R. Srikant

AISTATS 2022 Improved Algorithms for Misspecified Linear Markov Decision Processes Daniel Vial, Advait Parulekar, Sanjay Shakkottai, R Srikant

NeurIPS 2022 Minimax Regret for Cascading Bandits Daniel Vial, Sujay Sanghavi, Sanjay Shakkottai, R. Srikant

ICML 2022 Regret Bounds for Stochastic Shortest Path Problems with Linear Function Approximation Daniel Vial, Advait Parulekar, Sanjay Shakkottai, R Srikant

L4DC 2021 The Dynamics of Gradient Descent for Overparametrized Neural Networks Siddhartha Satpathi, R Srikant

AISTATS 2020 Budget-Constrained Bandits over General Cost and Reward Distributions Semih Cayci, Atilla Eryilmaz, R Srikant

NeurIPS 2020 The Mean-Squared Error of Double Q-Learning Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant

COLT 2019 Finite-Time Error Bounds for Linear Stochastic Approximation andTD Learning R. Srikant, Lei Ying

NeurIPS 2019 Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning Harsh Gupta, R. Srikant, Lei Ying

NeurIPS 2018 Adding One Neuron Can Eliminate All Bad Local Minima Shiyu Liang, Ruoyu Sun, Jason Lee, R. Srikant

ICLR 2018 Enhancing the Reliability of Out-of-Distribution Image Detection in Neural Networks Shiyu Liang, Yixuan Li, R. Srikant

ICLR 2017 Why Deep Neural Networks for Function Approximation? Shiyu Liang, R. Srikant

NeurIPS 2015 Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits Huasen Wu, R. Srikant, Xin Liu, Chong Jiang

MLJ 2014 Collaborative Filtering with Information-Rich and Information-Sparse Entities Kai Zhu, Rui Wu, Lei Ying, R. Srikant