Journal Papers     

1. 1. Distributed Grid Optimization via Distributed Dual Subgradient Methods with Averaging. S. Bose, H. D. Nguyen, H. Liu, Y. Guo, T. T. Doan, C. L. Beck. Journal of Optimization Theory and Applications, 2004. 

   2. A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning. S. Zeng, T. T. Doan, J. Romberg. SIAM on Optimization, 2023. 

   3. Finite-Time Convergence Rates of Distributed Local Stochastic Approximation. T. T. Doan. Automatica, 2023. 

   4. Federated Multi-Agent Deep Reinforcement Learning (Fed-MADRL) for Dynamic Spectrum Access. H-H. Chang, Y. Song, T. T. Doan, and L. Liu. IEEE Transactions on Wireless Communications, 2022.

   5.  Byzantine Fault-Tolerance in Federated Local SGD under 2f-Redundancy. N. Gupta, T. T. Doan, N. Vaidya. IEEE Transactions on Control of Network Systems, 2022.

   6.  Finite-Time Analysis of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning. S.    Zeng, T. T. Doan, J. Romberg. IEEE Transactions on Automatic Control - Special Issue on Learning and Control, 2022.

   7.  Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance. T. T. Doan. IEEE Transactions on Automatic Control, 2022.       

   8.  Finite-Time Analysis of Markov Gradient Descent. T. T. Doan. IEEE Transactions on Automatic Control, 2022. 

   9.  Finite Sample Analysis of Two-Time-Scale Natural Actor-Critic Algorithm. S. Khodadadian, T. T. Doan, S. T. Maguluri, J. Romberg. IEEE Transactions on Automatic Control, 2022.

   10.  Performance of Q-learning with Linear Function Approximation: Stability and Finite-Time Analysis. Z. Chen, S. Zhang, T. T. Doan, S. T. Maguluri, J-P. Clarke. Automatica, 2022.

   11.  A Reinforcement Learning Framework for Sequencing Multi-Robot Behaviors. P. Pierpaoli, T. T. Doan, J. Romberg, M. Egerstedt. Control Theory and Technology, 2021. 

   12.  Finite-Time Analysis and Restarting Scheme for Linear Two-Time-Scale Stochastic Approximation. T. T. Doan. SIAM Journal on Control and Optimization, vol. 59, no. 4, p. 2798-2819, 2021. 

   13.  Finite-Time Performance of Distributed Temporal Difference Learning with Linear Function Approximation. T. T. Doan, S. T. Maguluri, J. Romberg. SIAM Journal on Mathematics of Data Science, vol. 3, pp. 298-320, 2021.  

   14.  Distributed Resource Allocation Over Dynamic Networks with Uncertainty. T. T. Doan, C. L. Beck. IEEE Transactions on Automatic Control, vol. 66, no. 9, pp. 4378-4384, Sept. 2021.

   15. Convergence Rates of Distributed Gradient Methods Under Random Quantization: A Stochastic Approximation Approach. T. T. Doan, S. T. Maguluri, J. Romberg. IEEE Transactions on Automatic Control, vol. 66, no. 10, pp. 4469-4484, Oct. 2021.

   16. Fast Convergence Rates of Distributed Subgradient Methods with Adaptive Quantization. T. T. Doan, S. T. Maguluri, J. Romberg. IEEE Transactions on Automatic Control, vol. 66, no. 5, pp. 2191-2205, May 2021.

   17. Convergence of the Iterates in Mirror Descent Methods. T. T. Doan, S. Bose, D. H. Nguyen, C. L. Beck. IEEE Control Systems Letters, vol. 3, issue 1, 2019.

   18. On the Convergence Rate of Distributed Gradient Methods for Finite-Sum Optimization under Communication Delays. T. T. Doan, C. L. Beck, R. Srikant. Proceedings of the ACM on Measurement and Analysis of Computing Systems - SIGMETRICS, vol. 1, issue 2, 2017. 

   19. Distributed Resource Allocation on Dynamic Networks in Quadratic Time. T. T. Doan, A. Olshevsky. Systems & Control Letters, vol. 99, pp 57–63, Jan. 2017.

Conference Proceedings

1. Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems. S. Zeng, T. T. Doan, J. Romberg. Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023.  

2. Resilient Federated Learning under Byzantine Attack in Distributed Nonconvex Optimization with 2-f Redundancy. A. Dutta, T. T. Doan, J. Reed. 2023 IEEE Conference on Decision and Control (CDC).

3. Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games. Sihan Zeng, Thinh T. Doan, Justin Romberg. Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), 2022. 

4. Finite-Time Complexity of Online Primal-Dual Natural Actor-Critic Algorithm for Constrained Markov Decision Processes. Sihan Zeng, Thinh T. Doan, Justin Romberg. 2022 IEEE Conference on Decision and Control (CDC).

5. Convergence Rates of Decentralized Gradient Methods over Cluster Networks. A. Dutta, N. Masrourisaadat, T. T. Doan. 2022 IEEE Conference on Decision and Control (CDC).

6. Convergence Rates of Distributed Consensus over Cluster Networks: A Two-Time-Scale Approach. A. Dutta, A. M. Boker, T. T. Doan. 2022 IEEE Conference on Decision and Control (CDC).

7. Convergence Rates of Asynchronous Policy Iteration for Zero-Sum Markov Games under Stochastic and Optimistic Settings. S. Brahma, Y. Bai, D. A. Do, T. T. Doan. 2022 IEEE Conference on Decision and Control (CDC).

8. Convergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems. Thinh T. Doan. Accepted at 4th Annual Learning for Dynamics & Control Conference, 2022.

9. Distributed Local Two-Time-Scale Stochastic Approximation. T. T. Doan. Indian Control Conference (ICC-7), 2021.

10. Finite-Time Analysis of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning. S. Zeng, T. T. Doan, J. Romberg.  2021 IEEE Conference on Decision and Control (CDC).

11. Improved Convergence Rate for a Distributed Two-Time-Scale Gradient Method under Random Quantization. M. M. Vasconcelos, T. T. Doan, U. Mitra. 2021 IEEE Conference on Decision and Control (CDC).

12. A Decentralized Policy Gradient Approach to Multi-task Reinforcement Learning. S. Zeng, A. Anwar, T. T. Doan, J. Romberg, A. Raychowdhury. 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021.

13. Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance. T. T. Doan. 3rd Annual Learning for Dynamics & Control Conference, 2021. Extended abstract.

14.  Distributed two-time-scale methods over clustered networks. T. V. Pham, T. T. Doan, D. H. Nguyen. Proceedings of American Control Conference, 2021.

15. Byzantine fault-tolerance in decentralized optimization under minimal redundancy. N. Gupta, T. T. Doan, N. H. Vaidya. Proceedings of American Control Conference, 2021.

16. Finite-Time Performance of Distributed Two-Time-Scale Stochastic Approximation. T. T. Doan, J. Romberg. Proceedings of Learning for Dynamics & Control (L4DC), Berkeley, CA, 2020.

17. Linear Two-Time-Scale  Stochastic Approximation: A Finite-Time Analysis. T. T. Doan, J. Romberg. Proceedings of Allerton Conference on Communication, Control, and Computing, Monticello, IL, 2019.

18. Convergence Rates of Distributed Two-Time-Scale Gradient Methods under Random Quantization. T. T. Doan, S. T. Maguluri, J. Romberg. Proceedings of 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys19).

19. Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation for Multi-Agent Reinforcement Learning. T. T. Doan, S. T. Maguluri, J. Romberg. Proceedings of the 36th International Conference on Machine Learning (ICML), 2019.

20. Convergence Rate of Distributed Consensus with Nonuniform Delays. T. T. Doan, C. L. Beck, R. Srikant. Proceedings of Asilomar Conference on Signals, Systems, and Computers, 2018. 

21. On the Convergence of Distributed Subgradient Methods under Quantization. T. T. Doan, S. T. Maguluri, J. Romberg. Proceedings of Allerton Conference on Communication, Control, and Computing, Monticello, IL, 2018.

22. Convergence Rate of Distributed Random Projections. T. T. Doan, J. Lubars, C. L. Beck, R. Srikant. Proceedings of 7th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys18). 

23. Convergence Rate of Distributed Subgradient Methods under Communication Delays. T. T. Doan, C. L. Beck, R. Srikant. Proceedings of American Control Conference, Milwaukee, WI, 2018.

24. Aggregating Stochastic Gradients in Distributed Optimization. T. T. Doan. Proceedings of American Control Conference, Milwaukee, WI, 2018.  

25. Distributed Lagrangian Methods for Network Resource Allocation. T. T. Doan, C. L. Beck. Proceedings of 1st IEEE Conference on Control and Technology Applications, Kohala Coast, HI, 2017.

26. Distributed Lagrangian Method for Tie-Line Scheduling in Power Grids under Uncertainty. T. T. Doan, S. Bose, C. L. Beck. Proceedings of ACM SIGMETRICS Performance Evaluation Review (PER), vol. 45, issue 2, pp. 88-90, 2017. Extended abstract.

27. Continuous-Time Constrained Distributed Convex Optimization. T. T. Doan, C. Y. Tang. Proceedings of Allerton Conference on Communication, Control, and Computing, pp. 1482 – 1489, Monticello, IL, 2012.

Preprints/Under Submissions

1. 1. Natural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning. S. Zeng, T. T. Doan, J. Romberg.

   2. Fast Nonlinear Two-Time-Scale Stochastic Approximation: Achieving O(1/k) Finite-Sample Complexity. T. T. Doan.

   3. Finite-Time Convergence Rates of Nonlinear Two-Time-Scale Stochastic Approximation under Markovian Noise. T. T. Doan.

   4. Convergence Rates of Accelerated Markov Gradient Descent with Applications in Reinforcement Learning. T. T. Doan, L. M. Nguyen, N. H. Pham, J. Romberg.