Y. Du, R. Srikant, W. Chen. Cascading Reinforcement Learning, ICLR 2024 (Spotlight Presentation)
Y. Murthy, M. Moharrami and R. Srikant. Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms, NeurIPS 2023.
Y. Murthy, M. Moharrami, and R. Srikant. Modified Policy Iteration for Exponential Cost Risk Sensitive MDPs. L4DC 2023.
Z. Yang, R. Srikant, and L. Ying. MaxWeight With Discounted UCB: A Provably Stable Scheduling Policy for Nonstationary Multi-Server Systems With Unknown Statistics. AISTATS 2023.
A. Winnicki and R. Srikant. On The Convergence Of Policy Iteration-Based Reinforcement Learning With Monte Carlo Policy Evaluation. AISTATS 2023
X. Xie, D. Katselis, C.L. Beck and R. Srikant. Finite Sample Analysis for Structured Discrete System Identification. IEEE Transactions on Automatic Control, 2023.
S. Cayci, S. Satpathi, N. He and R. Srikant. Sample Complexity and Overparameterization Bounds for Temporal Difference Learning with Neural Network Approximation. IEEE Transactions on Automatic Control, 2023.
D. Vial, S. Shakkottai, R. Srikant, Robust multi-agent bandits over undirected graphs, ACM SIGMETRICS 2023.
D. Vial, S. Sanghavi, S. Shakkottai, R. Srikant, Minimax regret for cascading bandits, NeurIPS 2022.
D. Vial, A. Parulekar, S. Shakkottai, R. Srikant,.Improved algorithms for misspecified linear Markov decision processes., AISTATS 2022.
D. Vial, A. Parulekar, S. Shakkottai, R. Srikant, Regret bounds for stochastic shortest path problems with linear function approximation. ICML 2022.
D. Vial, S. Shakkottai, R. Srikant. Robust multi-agent multi-armed bandits. MobiHoc 2021.
D. Katselis, X. Xie, C. Beck and R. Srikant. On Concentration Inequalities for Vector-Valued Lipschitz Functions. Statistics and Probability Letters, 2021.
X. Xie, D. Katselis, C. Beck and R. Srikant. On the Consistency of Maximum Likelihood Estimators for Causal Network Identification. IEEE Control System Letters, 2021.
W. Weng, H. Gupta, N. He, L. Ying and R. Srikant. The Mean-Squared Error of Double Q-Learning, NeurIPS 2020. Longer version in arxiv. Talk given at the Simons Institute.
S. Liang, R. Sun and R. Srikant. Revisiting Landscape Analysis in Deep Neural Networks. arxiv, 2019, To appear in the SIAM Journal on Optimization. Slides of talk given at MIIS 2019.
H. Gupta, R. Srikant, L. Ying. Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning , NeurIPS, 2019.
R. Srikant and L. Ying. Finite-Time Error Bounds for Linear Stochastic Approximation and TD Learning. Conference on Learning Theory (COLT), 2019.
H. Gupta, A. Eryilmaz, and R. Srikant. Link Rate Selection using Constrained Thompson Sampling, IEEE INFOCOM 2019.
S. Satpathi, S. Deb, R. Srikant, and H. Yan. Learning Latent Events from Network Message Logs. Proc. 4th Workshop on Mining and Learning from Time Series (Held in Conjunction with KDD), 2018. Longer version in IEEE/ACM Transactions on Networking, 2019.
S. Liang, R. Sun, J. Lee, and R. Srikant. Adding One Neuron Can Eliminate All Bad Local Minima. NeurIPS 2018. Slides of talk given at IMACCS 2018.
S. Liang, R. Sun, Y. Li, and R. Srikant. Understanding the Loss Surface of Neural Networks for Binary Classification, ICML 2018.
T. T. Doan, C. L. Beck, and R. Srikant. On the Convergence Rate of Distributed Gradient Methods for Finite-Sum Optimization under Communication Delays, ACM SIGMETRICS 2018.
H. Gupta, A. Eryilmaz, and R. Srikant. Low-Complexity, Low-Regret Link Rate Selection in Rapidly Time-Varying Wireless Channels. Proc. IEEE INFOCOM, 2018.
J. Lubars and R. Srikant. Correcting the Output of Approximate Graph Matching Algorithms. Proc. IEEE INFOCOM 2018. Slides of Talk at Applied Probability Society Conference, 2017.
S. Liang, Y. Li and R. Srikant. Enhancing the Reliability of Out-of-Distribution Image Detection in Neural Networks, ICLR 2018.
S. Liang and R. Srikant. Why Deep Neural Networks for Function Approximation?, 5th International Conference on Learning Representations (ICLR), 2017. Earlier version in arxiv, 2016. Slides, Video from Talk at Workshop on Cognition and Control, Jan. 2017
D. Katselis, C. Beck and R. Srikant. Mixing Times and Structural Inference for Autoregressive Processes. IEEE Transactions on Network Science and Engineering, 2018, arxiv: 2016.
B. Dembin. S. Satpathi, and R. Srikant. Perfect Clustering from Pairwise Comparisons, 2016. Video of Talk at the Simons Institute for the Theory of Computing, Berkeley.
H. Wu, X. Li, R. Srikant, C. Jiang. Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits, NeurIPS 2015.
R. Wu, R. Srikant, and J. Ni. Learning Loosely Connected Markov Random Fields. Stochastic Systems, 2013.
C. L. Beck and R. Srikant. Error Bounds for Constant Step-size Q-Learning, Systems and Control Letters, 2012.