no code implementations • 2 Jan 2024 • Aritra Mitra, Lintao Ye, Vijay Gupta
Toward answering this question, we study a setting where a worker agent transmits quantized policy gradients (of the LQR cost) to a server over a noiseless channel with a finite bit-rate.
no code implementations • 8 Feb 2023 • Lei Xin, Lintao Ye, George Chiu, Shreyas Sundaram
We use a weighted least squares approach, and provide finite sample error bounds of the learned model as a function of the number of samples and various system parameters from the two systems as well as the weight assigned to the auxiliary data.
no code implementations • 17 Oct 2022 • Lintao Ye, Ming Chi, Ruiquan Liao, Vijay Gupta
Under the assumption that the system is stable or given a known stabilizing controller, we show that our controller enjoys an expected regret that scales as $\sqrt{T}$ with the time horizon $T$ for the case of partially nested information pattern.
1 code implementation • 11 Apr 2022 • Lei Xin, Lintao Ye, George Chiu, Shreyas Sundaram
We study the problem of identifying the dynamics of a linear system when one has access to samples generated by a similar (but not identical) system, in addition to data from the true system.
no code implementations • 14 Oct 2021 • Lintao Ye, Hao Zhu, Vijay Gupta
We study the problem of control policy design for decentralized state-feedback linear quadratic control with a partially nested information structure, when the system model is unknown.
no code implementations • 11 May 2021 • Lintao Ye, Philip E. Paré, Shreyas Sundaram
We study the problem of estimating the parameters (i. e., infection rate and recovery rate) governing the spread of epidemics in networks.
no code implementations • 21 Nov 2020 • Lintao Ye, Aritra Mitra, Shreyas Sundaram
We then show that the data source selection problem can be transformed into an instance of the submodular set covering problem studied in the literature, and provide a standard greedy algorithm to solve the data source selection problem with provable performance guarantees.