1 citations · 2 across the 3 of their papers we have counts for
6 papers
Encrypted Distributed Lasso for Sparse Data Predictive Control
Andreea B. Alexandru, Anastasios Tsiamis, George J. Pappas
The least squares problem with L1-regularized regressors, called Lasso, is a widely used approach in optimization problems where sparsity of the regressors is desired. This formula…
Linear Systems can be Hard to Learn
Anastasios Tsiamis, George J. Pappas
In this paper, we investigate when system identification is statistically easy or hard, in the finite sample regime. Statistically easy to learn linear system classes have sample c…
Sparse Approximate Solutions to Max-Plus Equations with Application to Multivariate Convex Regression
Nikos Tsilivis, Anastasios Tsiamis, Petros Maragos
In this work, we study the problem of finding approximate, with minimum support set, solutions to matrix max-plus equations, which we call sparse approximate solutions. We show how…
Risk-Constrained Linear-Quadratic Regulators
Anastasios Tsiamis, Dionysios S. Kalogerias, Luiz F. O. Chamon +2
We propose a new risk-constrained reformulation of the standard Linear Quadratic Regulator (LQR) problem. Our framework is motivated by the fact that the classical (risk-neutral) L…
Online Learning of the Kalman Filter with Logarithmic Regret
Anastasios Tsiamis, George Pappas
In this paper, we consider the problem of predicting observations generated online by an unknown, partially observed linear system, which is driven by stochastic noise. For such sy…
Sample Complexity of Kalman Filtering for Unknown Systems
Anastasios Tsiamis, Nikolai Matni, George J. Pappas
In this paper, we consider the task of designing a Kalman Filter (KF) for an unknown and partially observed autonomous linear time invariant system driven by process and sensor noi…