Selected Publications

Chen, Z., Zubeldia, M., & Maguluri, S. T. (2023). Concentration of Contractive Stochastic Approximation: Additive and Multiplicative Noise. Under review by The Annals of Applied Probability [PDF].

Zhang, S., Zhang, Z., Chen, Z., & Maguluri, S. T. (2024). Finite-Sample Analysis of Seminorm Contractive Stochastic Approximation with Applications in Average-Reward Reinforcement Learning. Draft in progress.

Chen, Z., Zhang, K., Mazumdar, E., Ozdaglar, A., & Wierman, A. (2024). Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games. Under review [PDF].

Lu, C., Chen, Z., Shi, L., Wu, C., & Wierman, A. (2023). Sample Efficient Reinforcement Learning via Kernel Graph Decomposition. Under review.

Jin, R., Chen, Z., Lin, Y., Song, J., & Wierman, A. (2023). Approximate Global Convergence of Independent Learning in Multi-Agent Systems. Under review.

Chen, Z., & Mazumdar, E. (2023). Last-Iterate Convergence for Generalized Frank-Wolfe in Monotone Variational Inequalities. Under review.

Chen, Z., Maguluri, S. T., Shakkottai, S., & Shanmugam, K. (2023). A Lyapunov Theory for Finite-Sample Guarantees of Markovian Stochastic Approximation. Operations Research [PDF]. 

Chen, Z., Mou, S., & Maguluri, S. T. (2021). Stationary Behavior of Constant-Stepsize SGD Type Algorithms: An Asymptotic Characterization. ACM SIGMETRICS 2022, journal version published at the ACM on Measurement and Analysis of Computing Systems [PDF].

Chen, Z., Clarke, J.-P., & Maguluri, S. T. (2023). Target Network and Truncation Overcome The Deadly Triad in Q-Learning. The SIAM Journal on Mathematics of Data Science [PDF]. 

Chen, Z., Zhang, S., Doan, T. T., Clarke, J.-P., & Maguluri, S. T. (2022). Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement Learning. Automatica [PDF].

Chen, Z., Khodadadian, S., & Maguluri, S. T. (2022). Finite-Sample Analysis of Off-Policy Natural Actor-Critic with Linear Function Approximation. IEEE Control Systems Letters (L-CSS), conference version accepted by CDC 2022 [PDF].

Chen, Z., & Maguluri, S. T. (2022). Sample Complexity of Policy Space Methods under Off-Policy Sampling and Linear Function Approximation. The International Conference on Artificial Intelligence and Statistics 2022 [PDF].

Chen, Z., Maguluri, S. T., Shakkottai, S., & Shanmugam, K. (2021). Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators. The Conference on Neural Information Processing Systems 2021 [PDF].

Chen, Z., Khodadadian, S., & Maguluri, S. T. (2021). Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm. The International Conference on Machine Learning 2021 [PDF].

Chen, Z., Maguluri, S. T., Shakkottai, S., & Shanmugam, K. (2020). Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes. The Conference on Neural Information Processing Systems 2020 [PDF].

Chen, Z., Zhang, K., Mazumdar, E., Ozdaglar, A., & Wierman, A. (2023). A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games. The Conference on Neural Information Processing Systems 2023 [PDF].

Zhou, Z., Chen, Z., Lin, Y., & Wierman, A. (2023). Convergence Rates for Localized Actor-Critic in Networked Markov Potential Games. The Conference on Uncertainty in Artificial Intelligence [PDF].

Zhang, Y., Qu, G., Xu, P., Lin, Y., Chen, Z., & Wierman, A. (2023). Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning. The ACM on Measurement and Analysis of Computing Systems, conference version accepted by SIGMETRICS 2023 [PDF].