4 papers · 1 filter
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…
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…