Continuous-time constrained distributed convex optimization. - Thinh Thanh Doan; Choon Yik Tang (Allerton Conference, 2012)

Distributed Lagrangian methods for network resource allocation. - Thinh T. Doan 001; Carolyn L. Beck (CCTA, 2017)

On the Convergence Rate of Distributed Gradient Methods for Finite-Sum Optimization under Communication Delays. - Thinh T. Doan 001; Carolyn L. Beck; R. Srikant 001 (Proc. ACM Meas. Anal. Comput. Syst., 2017)

Distributed resource allocation on dynamic networks in quadratic time. - Thinh T. Doan 001; Alex Olshevsky (Syst. Control. Lett., 2017)

Distributed Lagrangian Method for Tie-Line Scheduling in Power Grids under Uncertainty. - Thinh T. Doan 001; Subhonmesh Bose; Carolyn L. Beck (SIGMETRICS Perform. Evaluation Rev., 2017)

Convergence Rate of Distributed Consensus with Nonuniform Delays. - Thinh T. Doan 001; Carolyn L. Beck; R. Srikant 001 (ACSSC, 2018)

On the Convergence of Distributed Subgradient Methods under Quantization. - Thinh T. Doan 001; Siva Theja Maguluri; Justin Romberg (Allerton, 2018)

Aggregating Stochastic Gradients in Distributed Optimization. - Thinh T. Doan 001 (ACC, 2018)

Convergence Rate of Distributed Subgradient Methods under Communication Delays. - Thinh T. Doan 001; Carolyn L. Beck; R. Srikant 001 (ACC, 2018)

On the performance of distributed algorithms for network optimization problems - Thinh Thanh Doan (2018)

Linear Two-Time-Scale Stochastic Approximation A Finite-Time Analysis. - Thinh T. Doan 001; Justin Romberg (Allerton, 2019)

Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning. - Thinh T. Doan 001; Siva Theja Maguluri; Justin Romberg (ICML, 2019)

Finite-Time Analysis of Q-Learning with Linear Function Approximation. - Zaiwei Chen; Sheng Zhang; Thinh T. Doan 001; Siva Theja Maguluri; John-Paul Clarke (CoRR, 2019)

A Reinforcement Learning Framework for Sequencing Multi-Robot Behaviors. - Pietro Pierpaoli; Thinh T. Doan 001; Justin Romberg; Magnus Egerstedt (CoRR, 2019)

Convergence of the Iterates in Mirror Descent Methods. - Thinh T. Doan 001; Subhonmesh Bose; Dinh Hoa Nguyen; Carolyn L. Beck (IEEE Control. Syst. Lett., 2019)

Finite-Time Performance of Distributed Two-Time-Scale Stochastic Approximation. - Thinh T. Doan 001; Justin Romberg (L4DC, 2020)

Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness. - Thinh T. Doan 001; Lam M. Nguyen; Nhan H. Pham; Justin Romberg (CoRR, 2020)

Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithms. - Thinh T. Doan 001 (CoRR, 2020)

Byzantine Fault-Tolerance in Decentralized Optimization under Minimal Redundancy. - Nirupam Gupta; Thinh T. Doan 001; Nitin H. Vaidya (CoRR, 2020)

Byzantine Fault-Tolerance in Decentralized Optimization under 2f-Redundancy. - Nirupam Gupta; Thinh T. Doan 001; Nitin H. Vaidya (ACC, 2021)

Distributed two-time-scale methods over clustered networks. - Van Thiem Pham; Thinh T. Doan 001; Dinh Hoa Nguyen (ACC, 2021)

Improved Convergence Rate for a Distributed Two-Time-Scale Gradient Method under Random Quantization. - Marcos M. Vasconcelos; Thinh T. Doan 001; Urbashi Mitra (CDC, 2021)

Finite-Time Analysis of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning. - Sihan Zeng; Thinh T. Doan 001; Justin Romberg (CDC, 2021)

A Resilient and Robust Edge-Cloud Network System Supporting CPS. - Tanmoy Sen; Haiying Shen; Walid Saad; Thinh T. Doan 001 (MASS, 2021)

A decentralized policy gradient approach to multi-task reinforcement learning. - Sihan Zeng; Malik Aqeel Anwar; Thinh T. Doan 001; Arijit Raychowdhury; Justin Romberg (UAI, 2021)

Finite-Time Convergence Rates of Nonlinear Two-Time-Scale Stochastic Approximation under Markovian Noise. - Thinh T. Doan 001 (CoRR, 2021)

Distributed Grid Optimization via Distributed Dual Subgradient Methods with Averaging. - Subhonmesh Bose; Hoa Dinh Nguyen; Haitian Liu; Ye Guo; Thinh T. Doan 001; Carolyn L. Beck (CoRR, 2021)

Finite-Time Analysis and Restarting Scheme for Linear Two-Time-Scale Stochastic Approximation. - Thinh T. Doan 001 (SIAM J. Control. Optim., 2021)

Finite-Time Performance of Distributed Temporal-Difference Learning with Linear Function Approximation. - Thinh T. Doan 001; Siva Theja Maguluri; Justin Romberg (SIAM J. Math. Data Sci., 2021)

Distributed Resource Allocation Over Dynamic Networks With Uncertainty. - Thinh T. Doan 001; Carolyn L. Beck (IEEE Trans. Autom. Control., 2021)

Fast Convergence Rates of Distributed Subgradient Methods With Adaptive Quantization. - Thinh T. Doan 001; Siva Theja Maguluri; Justin Romberg (IEEE Trans. Autom. Control., 2021)

Convergence Rates of Distributed Gradient Methods Under Random Quantization: A Stochastic Approximation Approach. - Thinh T. Doan 001; Siva Theja Maguluri; Justin Romberg (IEEE Trans. Autom. Control., 2021)

Convergence Rates of Asynchronous Policy Iteration for Zero-Sum Markov Games under Stochastic and Optimistic Settings. - Sarnaduti Brahma; Yitao Bai; Duy Anh Do; Thinh T. Doan 001 (CDC, 2022)

Convergence Rates of Distributed Consensus over Cluster Networks: A Two-Time-Scale Approach. - Amit Dutta; Almuatazbellah M. Boker; Thinh T. Doan 001 (CDC, 2022)

Convergence Rates of Decentralized Gradient Dynamics over Cluster Networks: Multiple-Time-Scale Lyapunov Approach. - Amit Dutta; Nila Masrourisaadat; Thinh T. Doan 001 (CDC, 2022)

Finite-Time Complexity of Online Primal-Dual Natural Actor-Critic Algorithm for Constrained Markov Decision Processes. - Sihan Zeng; Thinh T. Doan 001; Justin Romberg (CDC, 2022)

Convergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems. - Thinh T. Doan 001 (L4DC, 2022)

Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games. - Sihan Zeng; Thinh T. Doan 001; Justin Romberg (NeurIPS, 2022)

Finite-sample analysis of nonlinear stochastic approximation with applications in reinforcement learning. - Zaiwei Chen; Sheng Zhang; Thinh T. Doan 001; John-Paul Clarke; Siva Theja Maguluri (Autom., 2022)

Convergence and Price of Anarchy Guarantees of the Softmax Policy Gradient in Markov Potential Games. - Dingyang Chen 001; Qi Zhang 038; Thinh T. Doan 001 (CoRR, 2022)

Resilient Federated Learning Under Byzantine Attack in Distributed Nonconvex Optimization with 2-$f$ Redundancy. - Amit Dutta; Thinh T. Doan 001; Jeffrey H. Reed (CDC, 2023)

Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems. - Sihan Zeng; Thinh T. Doan 001; Justin Romberg (NeurIPS, 2023)

Finite-Time Analysis of Markov Gradient Descent. - Thinh T. Doan 001 (IEEE Trans. Autom. Control., 2023)

Finite-Time Convergence Rates of Decentralized Stochastic Approximation With Applications in Multi-Agent and Multi-Task Learning. - Sihan Zeng; Thinh T. Doan 001; Justin Romberg (IEEE Trans. Autom. Control., 2023)

Finite-Sample Analysis of Two-Time-Scale Natural Actor-Critic Algorithm. - Sajad Khodadadian; Thinh T. Doan 001; Justin Romberg; Siva Theja Maguluri (IEEE Trans. Autom. Control., 2023)

Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance. - Thinh T. Doan 001 (IEEE Trans. Autom. Control., 2023)

Federated Multi-Agent Deep Reinforcement Learning (Fed-MADRL) for Dynamic Spectrum Access. - Hao-Hsuan Chang; Yifei Song; Thinh T. Doan 001; Lingjia Liu 001 (IEEE Trans. Wirel. Commun., 2023)

Finite-time convergence rates of distributed local stochastic approximation. - Thinh T. Doan 001 (Autom., 2023)

Byzantine Fault-Tolerance in Federated Local SGD Under $2f$-Redundancy. - Nirupam Gupta; Thinh T. Doan 001; Nitin H. Vaidya (IEEE Trans. Control. Netw. Syst., 2023)

Bayesian meta learning for trustworthy uncertainty quantification. - Zhenyuan Yuan; Thinh T. Doan 001 (CDC, 2024)

Fast two-time-scale stochastic gradient method with applications in reinforcement learning. - Sihan Zeng; Thinh T. Doan 001 (COLT, 2024)

Convergence Rates of Gradient Descent-Ascent Dynamics Under Delays in Solving Nonconvex Min-Max Optimization. - Duy Anh Do; Thinh T. Doan 001 (ECC, 2024)

Finite-time complexity of incremental policy gradient methods for solving multi-task reinforcement learning. - Yitao Bai; Thinh T. Doan 001 (L4DC, 2024)

Fast Nonlinear Two-Time-Scale Stochastic Approximation: Achieving O(1/k) Finite-Sample Complexity. - Thinh T. Doan 001 (CoRR, 2024)

Natural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning. - Sihan Zeng; Thinh T. Doan 001; Justin Romberg (CoRR, 2024)

On the O(1/k) Convergence of Distributed Gradient Methods Under Random Quantization. - Amit Dutta; Thinh T. Doan 001 (IEEE Control. Syst. Lett., 2024)

A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning. - Sihan Zeng; Thinh T. Doan 001; Justin Romberg (SIAM J. Optim., 2024)

Distributed Dual Subgradient Methods with Averaging and Applications to Grid Optimization. - Haitian Liu; Subhonmesh Bose; Dinh Hoa Nguyen; Ye Guo; Thinh T. Doan 001; Carolyn L. Beck (J. Optim. Theory Appl., 2024)

Distributed Optimization in Open Networks under Redundancy. - Amit Dutta; Thinh T. Doan 001 (ACC, 2025)

Nonasymptotic CLT and Error Bounds for Two-Time-Scale Stochastic Approximation. - Seo Taek Kong; Sihan Zeng; Thinh T. Doan 001; R. Srikant 001 (CoRR, 2025)

Accelerating Multi-Task Temporal Difference Learning under Low-Rank Representation. - Yitao Bai; Sihan Zeng; Justin Romberg; Thinh T. Doan 001 (CoRR, 2025)