6 papers
Distributed Online Bandit Submodular Maximization with Bounded Sampling Violations
Bin Du, Chang Liu, Dingqi Zhu +2
We study distributed online submodular maximization under partition matroid constraints, in which multiple agents select a limited number of actions from their own subsets sequenti…
Learning to Sparsify Stochastic Linear Bandits
Zhengmiao Wang, Ming Chi, Zhi-Wei Liu +2
This paper addresses the problem of learning to sparsify stochastic linear bandits, where a decision-maker sequentially selects actions from a high-dimensional space subject to a s…
Online Learning of Kalman Filtering: From Output to State Estimation
Lintao Ye, Ankang Zhang, Ming Chi +2
In this paper, we study the problem of learning Kalman filtering with unknown system model in partially observed linear dynamical systems. We propose a unified algorithmic framewor…
Model-Free Output Feedback Stabilization via Policy Gradient Methods
Ankang Zhang, Ming Chi, Xiaoling Wang +1
Stabilizing a dynamical system is a fundamental problem that serves as a cornerstone for many complex tasks in the field of control systems. The problem becomes challenging when th…
Online Convex Optimization with Memory and Limited Predictions
Zhengmiao Wang, Zhi-Wei Liu, Ming Chi +3
This paper addresses an online convex optimization problem where the cost function at each step depends on a history of past decisions (i.e., memory), and the decision maker has ac…
Learning Stabilizing Policies via an Unstable Subspace Representation
Leonardo F. Toso, Lintao Ye, James Anderson
We study the problem of learning to stabilize (LTS) a linear time-invariant (LTI) system. Policy gradient (PG) methods for control assume access to an initial stabilizing policy. H…