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ORCIDThinh T. Doan 0001
Person information
- affiliation: Virginia Tech, Bradley Department of Electrical and Computer Engineering, Arlington, VA, USA
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2020 – today
- 2024
[j18]Sihan Zeng
, Thinh T. Doan, Justin Romberg:
A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning. SIAM J. Optim. 34(1): 946-976 (2024)- [i23]Thinh T. Doan:
Fast Nonlinear Two-Time-Scale Stochastic Approximation: Achieving O(1/k) Finite-Sample Complexity. CoRR abs/2401.12764 (2024) - 2023
- [j17]Thinh T. Doan:
Finite-time convergence rates of distributed local stochastic approximation. Autom. 158: 111294 (2023) - [j16]Thinh T. Doan:
Finite-Time Analysis of Markov Gradient Descent. IEEE Trans. Autom. Control. 68(4): 2140-2153 (2023) - [j15]Sihan Zeng, Thinh T. Doan, Justin Romberg:
Finite-Time Convergence Rates of Decentralized Stochastic Approximation With Applications in Multi-Agent and Multi-Task Learning. IEEE Trans. Autom. Control. 68(5): 2758-2773 (2023) - [j14]Sajad Khodadadian, Thinh T. Doan, Justin Romberg, Siva Theja Maguluri:
Finite-Sample Analysis of Two-Time-Scale Natural Actor-Critic Algorithm. IEEE Trans. Autom. Control. 68(6): 3273-3284 (2023) - [j13]Thinh T. Doan:
Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance. IEEE Trans. Autom. Control. 68(8): 4695-4705 (2023) - [j12]Nirupam Gupta, Thinh T. Doan, Nitin H. Vaidya:
Byzantine Fault-Tolerance in Federated Local SGD Under $2f$-Redundancy. IEEE Trans. Control. Netw. Syst. 10(4): 1669-1681 (2023) - [j11]Hao-Hsuan Chang, Yifei Song, Thinh T. Doan, Lingjia Liu:
Federated Multi-Agent Deep Reinforcement Learning (Fed-MADRL) for Dynamic Spectrum Access. IEEE Trans. Wirel. Commun. 22(8): 5337-5348 (2023) - [c25]Amit Dutta, Thinh T. Doan, Jeffrey H. Reed:
Resilient Federated Learning Under Byzantine Attack in Distributed Nonconvex Optimization with 2-$f$ Redundancy. CDC 2023: 1156-1161 - [c24]Sihan Zeng, Thinh T. Doan, Justin Romberg:
Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems. NeurIPS 2023 - [i22]Sihan Zeng, Thinh T. Doan, Justin Romberg:
Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems. CoRR abs/2303.12981 (2023) - 2022
- [j10]Zaiwei Chen, Sheng Zhang, Thinh T. Doan, John-Paul Clarke, Siva Theja Maguluri:
Finite-sample analysis of nonlinear stochastic approximation with applications in reinforcement learning. Autom. 146: 110623 (2022) - [c23]Sarnaduti Brahma, Yitao Bai, Duy Anh Do, Thinh T. Doan:
Convergence Rates of Asynchronous Policy Iteration for Zero-Sum Markov Games under Stochastic and Optimistic Settings. CDC 2022: 3493-3498 - [c22]Sihan Zeng, Thinh T. Doan, Justin Romberg:
Finite-Time Complexity of Online Primal-Dual Natural Actor-Critic Algorithm for Constrained Markov Decision Processes. CDC 2022: 4028-4033 - [c21]Amit Dutta, Nila Masrourisaadat, Thinh T. Doan:
Convergence Rates of Decentralized Gradient Dynamics over Cluster Networks: Multiple-Time-Scale Lyapunov Approach. CDC 2022: 6497-6502 - [c20]Amit Dutta, Almuatazbellah M. Boker, Thinh T. Doan:
Convergence Rates of Distributed Consensus over Cluster Networks: A Two-Time-Scale Approach. CDC 2022: 7035-7040 - [c19]Thinh T. Doan:
Convergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems. L4DC 2022: 192-206 - [c18]Sihan Zeng, Thinh T. Doan, Justin Romberg:
Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games. NeurIPS 2022 - [i21]Sihan Zeng, Thinh T. Doan, Justin Romberg:
Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games. CoRR abs/2205.13746 (2022) - [i20]Dingyang Chen, Qi Zhang, Thinh T. Doan:
Convergence and Price of Anarchy Guarantees of the Softmax Policy Gradient in Markov Potential Games. CoRR abs/2206.07642 (2022) - 2021
- [j9]Thinh T. Doan:
Finite-Time Analysis and Restarting Scheme for Linear Two-Time-Scale Stochastic Approximation. SIAM J. Control. Optim. 59(4): 2798-2819 (2021) - [j8]Thinh T. Doan, Siva Theja Maguluri, Justin Romberg:
Finite-Time Performance of Distributed Temporal-Difference Learning with Linear Function Approximation. SIAM J. Math. Data Sci. 3(1): 298-320 (2021) - [j7]Thinh T. Doan, Siva Theja Maguluri, Justin Romberg:
Fast Convergence Rates of Distributed Subgradient Methods With Adaptive Quantization. IEEE Trans. Autom. Control. 66(5): 2191-2205 (2021) - [j6]Thinh T. Doan, Carolyn L. Beck:
Distributed Resource Allocation Over Dynamic Networks With Uncertainty. IEEE Trans. Autom. Control. 66(9): 4378-4384 (2021) - [j5]Thinh T. Doan, Siva Theja Maguluri, Justin Romberg:
Convergence Rates of Distributed Gradient Methods Under Random Quantization: A Stochastic Approximation Approach. IEEE Trans. Autom. Control. 66(10): 4469-4484 (2021) - [c17]Nirupam Gupta, Thinh T. Doan, Nitin H. Vaidya:
Byzantine Fault-Tolerance in Decentralized Optimization under 2f-Redundancy. ACC 2021: 3632-3637 - [c16]Van Thiem Pham, Thinh T. Doan, Dinh Hoa Nguyen:
Distributed two-time-scale methods over clustered networks. ACC 2021: 4625-4630 - [c15]Sihan Zeng, Thinh T. Doan, Justin Romberg:
Finite-Time Analysis of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning. CDC 2021: 2641-2646 - [c14]Marcos M. Vasconcelos, Thinh T. Doan, Urbashi Mitra:
Improved Convergence Rate for a Distributed Two-Time-Scale Gradient Method under Random Quantization. CDC 2021: 3117-3122 - [c13]Thinh T. Doan:
Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance. L4DC 2021: 47 - [c12]Tanmoy Sen, Haiying Shen, Walid Saad, Thinh T. Doan:
A Resilient and Robust Edge-Cloud Network System Supporting CPS. MASS 2021: 234-242 - [c11]Sihan Zeng, Malik Aqeel Anwar, Thinh T. Doan, Arijit Raychowdhury, Justin Romberg:
A decentralized policy gradient approach to multi-task reinforcement learning. UAI 2021: 1002-1012 - [i19]Sajad Khodadadian, Thinh T. Doan, Siva Theja Maguluri, Justin Romberg:
Finite Sample Analysis of Two-Time-Scale Natural Actor-Critic Algorithm. CoRR abs/2101.10506 (2021) - [i18]Thinh T. Doan:
Finite-Time Convergence Rates of Nonlinear Two-Time-Scale Stochastic Approximation under Markovian Noise. CoRR abs/2104.01627 (2021) - [i17]Marcos M. Vasconcelos, Thinh T. Doan, Urbashi Mitra:
Improved Convergence Rate for a Distributed Two-Time-Scale Gradient Method under Random Quantization. CoRR abs/2105.14089 (2021) - [i16]Subhonmesh Bose, Hoa Dinh Nguyen, Haitian Liu, Ye Guo, Thinh T. Doan, Carolyn L. Beck:
Distributed Grid Optimization via Distributed Dual Subgradient Methods with Averaging. CoRR abs/2107.07061 (2021) - [i15]Nirupam Gupta, Thinh T. Doan, Nitin H. Vaidya:
Byzantine Fault-Tolerance in Federated Local SGD under 2f-Redundancy. CoRR abs/2108.11769 (2021) - [i14]Sihan Zeng, Thinh T. Doan, Justin Romberg:
A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning. CoRR abs/2109.14756 (2021) - [i13]Sihan Zeng, Thinh T. Doan, Justin Romberg:
Finite-Time Complexity of Online Primal-Dual Natural Actor-Critic Algorithm for Constrained Markov Decision Processes. CoRR abs/2110.11383 (2021) - [i12]Thinh T. Doan:
Convergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems. CoRR abs/2112.09579 (2021) - 2020
- [c10]Thinh T. Doan, Justin Romberg:
Finite-Time Performance of Distributed Two-Time-Scale Stochastic Approximation. L4DC 2020: 26-36 - [i11]Thinh T. Doan, Lam M. Nguyen, Nhan H. Pham, Justin Romberg:
Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness. CoRR abs/2003.10973 (2020) - [i10]Sihan Zeng, Aqeel Anwar, Thinh T. Doan, Justin Romberg, Arijit Raychowdhury:
A Decentralized Policy Gradient Approach to Multi-task Reinforcement Learning. CoRR abs/2006.04338 (2020) - [i9]Thinh T. Doan:
Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithms. CoRR abs/2006.13460 (2020) - [i8]Nirupam Gupta, Thinh T. Doan, Nitin H. Vaidya:
Byzantine Fault-Tolerance in Decentralized Optimization under Minimal Redundancy. CoRR abs/2009.14763 (2020) - [i7]Van Thiem Pham, Thinh T. Doan, Dinh Hoa Nguyen:
Distributed two-time-scale methods over clustered networks. CoRR abs/2010.00355 (2020) - [i6]Sihan Zeng, Thinh T. Doan, Justin Romberg:
Finite-Time Analysis of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning. CoRR abs/2010.15088 (2020) - [i5]Thinh T. Doan:
Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance. CoRR abs/2011.01868 (2020)
2010 – 2019
- 2019
- [j4]Thinh T. Doan, Subhonmesh Bose, Dinh Hoa Nguyen, Carolyn L. Beck:
Convergence of the Iterates in Mirror Descent Methods. IEEE Control. Syst. Lett. 3(1): 114-119 (2019) - [c9]Thinh T. Doan, Justin Romberg:
Linear Two-Time-Scale Stochastic Approximation A Finite-Time Analysis. Allerton 2019: 399-406 - [c8]Thinh T. Doan, Siva Theja Maguluri, Justin Romberg:
Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning. ICML 2019: 1626-1635 - [i4]Zaiwei Chen, Sheng Zhang, Thinh T. Doan, Siva Theja Maguluri, John-Paul Clarke:
Finite-Time Analysis of Q-Learning with Linear Function Approximation. CoRR abs/1905.11425 (2019) - [i3]Thinh T. Doan, Siva Theja Maguluri, Justin Romberg:
Finite-Time Performance of Distributed Temporal Difference Learning with Linear Function Approximation. CoRR abs/1907.12530 (2019) - [i2]Pietro Pierpaoli, Thinh T. Doan, Justin Romberg, Magnus Egerstedt:
A Reinforcement Learning Framework for Sequencing Multi-Robot Behaviors. CoRR abs/1909.05731 (2019) - [i1]Thinh T. Doan:
Finite-Time Analysis and Restarting Scheme for Linear Two-Time-Scale Stochastic Approximation. CoRR abs/1912.10583 (2019) - 2018
- [b1]Thinh Thanh Doan:
On the performance of distributed algorithms for network optimization problems. University of Illinois Urbana-Champaign, USA, 2018 - [c7]Thinh T. Doan, Carolyn L. Beck, R. Srikant:
Convergence Rate of Distributed Consensus with Nonuniform Delays. ACSSC 2018: 1294-1298 - [c6]Thinh T. Doan, Siva Theja Maguluri, Justin Romberg:
On the Convergence of Distributed Subgradient Methods under Quantization. Allerton 2018: 567-574 - [c5]Thinh T. Doan:
Aggregating Stochastic Gradients in Distributed Optimization. ACC 2018: 2170-2175 - [c4]Thinh T. Doan, Carolyn L. Beck, R. Srikant:
Convergence Rate of Distributed Subgradient Methods under Communication Delays. ACC 2018: 5310-5315 - [c3]Thinh T. Doan, Carolyn L. Beck, R. Srikant:
On the Convergence Rate of Distributed Gradient Methods for Finite-Sum Optimization under Communication Delays. SIGMETRICS (Abstracts) 2018: 93-95 - 2017
- [j3]Thinh T. Doan, Carolyn L. Beck, R. Srikant:
On the Convergence Rate of Distributed Gradient Methods for Finite-Sum Optimization under Communication Delays. Proc. ACM Meas. Anal. Comput. Syst. 1(2): 37:1-37:27 (2017) - [j2]Thinh T. Doan, Alex Olshevsky:
Distributed resource allocation on dynamic networks in quadratic time. Syst. Control. Lett. 99: 57-63 (2017) - [j1]Thinh T. Doan, Subhonmesh Bose, Carolyn L. Beck:
Distributed Lagrangian Method for Tie-Line Scheduling in Power Grids under Uncertainty. SIGMETRICS Perform. Evaluation Rev. 45(2): 88-90 (2017) - [c2]Thinh T. Doan, Carolyn L. Beck:
Distributed Lagrangian methods for network resource allocation. CCTA 2017: 650-655 - 2012
- [c1]Thinh Thanh Doan, Choon Yik Tang:
Continuous-time constrained distributed convex optimization. Allerton Conference 2012: 1482-1489
Coauthor Index
aka: Justin Romberg
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