Thinh T. Doan

Orcid: 0000-0001-5135-3429

Affiliations:
  • Virginia Tech, Bradley Department of Electrical and Computer Engineering, Arlington, VA, USA


According to our database1, Thinh T. Doan authored at least 51 papers between 2012 and 2024.

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Bibliography

2024
A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning.
SIAM J. Optim., March, 2024

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

2023
Byzantine Fault-Tolerance in Federated Local SGD Under $2f$-Redundancy.
IEEE Trans. Control. Netw. Syst., December, 2023

Finite-time convergence rates of distributed local stochastic approximation.
Autom., December, 2023

Federated Multi-Agent Deep Reinforcement Learning (Fed-MADRL) for Dynamic Spectrum Access.
IEEE Trans. Wirel. Commun., August, 2023

Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance.
IEEE Trans. Autom. Control., August, 2023

Finite-Sample Analysis of Two-Time-Scale Natural Actor-Critic Algorithm.
IEEE Trans. Autom. Control., June, 2023

Finite-Time Convergence Rates of Decentralized Stochastic Approximation With Applications in Multi-Agent and Multi-Task Learning.
IEEE Trans. Autom. Control., May, 2023

Finite-Time Analysis of Markov Gradient Descent.
IEEE Trans. Autom. Control., 2023

Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Resilient Federated Learning Under Byzantine Attack in Distributed Nonconvex Optimization with 2-$f$ Redundancy.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

2022
Convergence and Price of Anarchy Guarantees of the Softmax Policy Gradient in Markov Potential Games.
CoRR, 2022

Finite-sample analysis of nonlinear stochastic approximation with applications in reinforcement learning.
Autom., 2022

Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Convergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems.
Proceedings of the Learning for Dynamics and Control Conference, 2022

Finite-Time Complexity of Online Primal-Dual Natural Actor-Critic Algorithm for Constrained Markov Decision Processes.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Convergence Rates of Decentralized Gradient Dynamics over Cluster Networks: Multiple-Time-Scale Lyapunov Approach.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Convergence Rates of Distributed Consensus over Cluster Networks: A Two-Time-Scale Approach.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Convergence Rates of Asynchronous Policy Iteration for Zero-Sum Markov Games under Stochastic and Optimistic Settings.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

2021
Convergence Rates of Distributed Gradient Methods Under Random Quantization: A Stochastic Approximation Approach.
IEEE Trans. Autom. Control., 2021

Fast Convergence Rates of Distributed Subgradient Methods With Adaptive Quantization.
IEEE Trans. Autom. Control., 2021

Distributed Resource Allocation Over Dynamic Networks With Uncertainty.
IEEE Trans. Autom. Control., 2021

Finite-Time Performance of Distributed Temporal-Difference Learning with Linear Function Approximation.
SIAM J. Math. Data Sci., 2021

Finite-Time Analysis and Restarting Scheme for Linear Two-Time-Scale Stochastic Approximation.
SIAM J. Control. Optim., 2021

Distributed Grid Optimization via Distributed Dual Subgradient Methods with Averaging.
CoRR, 2021

Finite-Time Convergence Rates of Nonlinear Two-Time-Scale Stochastic Approximation under Markovian Noise.
CoRR, 2021

A decentralized policy gradient approach to multi-task reinforcement learning.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

A Resilient and Robust Edge-Cloud Network System Supporting CPS.
Proceedings of the IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems, 2021

Finite-Time Analysis of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

Improved Convergence Rate for a Distributed Two-Time-Scale Gradient Method under Random Quantization.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

Distributed two-time-scale methods over clustered networks.
Proceedings of the 2021 American Control Conference, 2021

Byzantine Fault-Tolerance in Decentralized Optimization under 2f-Redundancy.
Proceedings of the 2021 American Control Conference, 2021

2020
Byzantine Fault-Tolerance in Decentralized Optimization under Minimal Redundancy.
CoRR, 2020

Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithms.
CoRR, 2020

Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness.
CoRR, 2020

Finite-Time Performance of Distributed Two-Time-Scale Stochastic Approximation.
Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

2019
Convergence of the Iterates in Mirror Descent Methods.
IEEE Control. Syst. Lett., 2019

A Reinforcement Learning Framework for Sequencing Multi-Robot Behaviors.
CoRR, 2019

Finite-Time Analysis of Q-Learning with Linear Function Approximation.
CoRR, 2019

Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning.
Proceedings of the 36th International Conference on Machine Learning, 2019

Linear Two-Time-Scale Stochastic Approximation A Finite-Time Analysis.
Proceedings of the 57th Annual Allerton Conference on Communication, 2019

2018
On the performance of distributed algorithms for network optimization problems
PhD thesis, 2018

Convergence Rate of Distributed Subgradient Methods under Communication Delays.
Proceedings of the 2018 Annual American Control Conference, 2018

Aggregating Stochastic Gradients in Distributed Optimization.
Proceedings of the 2018 Annual American Control Conference, 2018

On the Convergence of Distributed Subgradient Methods under Quantization.
Proceedings of the 56th Annual Allerton Conference on Communication, 2018

Convergence Rate of Distributed Consensus with Nonuniform Delays.
Proceedings of the 52nd Asilomar Conference on Signals, Systems, and Computers, 2018

2017
Distributed Lagrangian Method for Tie-Line Scheduling in Power Grids under Uncertainty.
SIGMETRICS Perform. Evaluation Rev., 2017

Distributed resource allocation on dynamic networks in quadratic time.
Syst. Control. Lett., 2017

On the Convergence Rate of Distributed Gradient Methods for Finite-Sum Optimization under Communication Delays.
Proc. ACM Meas. Anal. Comput. Syst., 2017

Distributed Lagrangian methods for network resource allocation.
Proceedings of the IEEE Conference on Control Technology and Applications, 2017

2012
Continuous-time constrained distributed convex optimization.
Proceedings of the 50th Annual Allerton Conference on Communication, 2012


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