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Convergence Rates of Distributed Gradient Methods Under Random Quantization: A Stochastic Approximation Approach.

, , and . IEEE Trans. Autom. Control., 66 (10): 4469-4484 (2021)

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Finite-Time Analysis of Markov Gradient Descent.. IEEE Trans. Autom. Control., 68 (4): 2140-2153 (2023)Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance.. IEEE Trans. Autom. Control., 68 (8): 4695-4705 (August 2023)Distributed two-time-scale methods over clustered networks., , and . ACC, page 4625-4630. IEEE, (2021)A Decentralized Policy Gradient Approach to Multi-task Reinforcement Learning., , , , and . CoRR, (2020)Finite-Time Analysis of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning., , and . CoRR, (2020)Finite-Time Analysis of Q-Learning with Linear Function Approximation., , , , and . CoRR, (2019)Finite-Time Analysis and Restarting Scheme for Linear Two-Time-Scale Stochastic Approximation.. SIAM J. Control. Optim., 59 (4): 2798-2819 (2021)Distributed Lagrangian methods for network resource allocation., and . CCTA, page 650-655. IEEE, (2017)Finite-Time Performance of Distributed Two-Time-Scale Stochastic Approximation., and . L4DC, volume 120 of Proceedings of Machine Learning Research, page 26-36. PMLR, (2020)Convergence Rates of Distributed Gradient Methods Under Random Quantization: A Stochastic Approximation Approach., , and . IEEE Trans. Autom. Control., 66 (10): 4469-4484 (2021)