Linear Systems can be Hard to Learn

Linear Systems can be Hard to Learn

2 April 2021

Anastasios Tsiamis

George J. Pappas

Papers citing "Linear Systems can be Hard to Learn"

11 / 11 papers shown

Title
Boosting-Enabled Robust System Identification of Partially Observed LTI Systems Under Heavy-Tailed Noise Vinay Kanakeri Aritra Mitra 29 0 0 25 Apr 2025
Spectral Statistics of the Sample Covariance Matrix for High Dimensional Linear Gaussians Muhammad Naeem Miroslav Pajic 18 0 0 10 Dec 2023
On the Hardness of Learning to Stabilize Linear Systems Xiong Zeng Zexiang Liu Zhe Du N. Ozay Mario Sznaier 29 3 0 18 Nov 2023
A Tutorial on the Non-Asymptotic Theory of System Identification Ingvar M. Ziemann Anastasios Tsiamis Bruce D. Lee Yassir Jedra Nikolai Matni George J. Pappas 30 25 0 07 Sep 2023
Learning and Concentration for High Dimensional Linear Gaussians: an Invariant Subspace Approach Muhammad Naeem 32 2 0 04 Apr 2023
FedSysID: A Federated Approach to Sample-Efficient System Identification Han Wang Leonardo F. Toso James Anderson FedML 29 17 0 25 Nov 2022
Statistical Learning Theory for Control: A Finite Sample Perspective Anastasios Tsiamis Ingvar M. Ziemann Nikolai Matni George J. Pappas 31 73 0 12 Sep 2022
Learning with little mixing Ingvar M. Ziemann Stephen Tu 34 27 0 16 Jun 2022
How are policy gradient methods affected by the limits of control? Ingvar M. Ziemann Anastasios Tsiamis H. Sandberg Nikolai Matni 25 14 0 14 Jun 2022
Learning to Control Linear Systems can be Hard Anastasios Tsiamis Ingvar M. Ziemann M. Morari Nikolai Matni George J. Pappas 26 14 0 27 May 2022
Active Learning in Robotics: A Review of Control Principles Annalisa T. Taylor Thomas A. Berrueta Todd D. Murphey 32 71 0 25 Jun 2021