7 citations · 10 across the 5 of their papers we have counts for
7 papers
State space models, emergence, and ergodicity: How many parameters are needed for stable predictions?
Ingvar Ziemann, Nikolai Matni, George J. Pappas
How many parameters are required for a model to execute a given task? It has been argued that large language models, pre-trained via self-supervised learning, exhibit emergent capa…
A Short Information-Theoretic Analysis of Linear Auto-Regressive Learning
Ingvar Ziemann
In this note, we give a short information-theoretic proof of the consistency of the Gaussian maximum likelihood estimator in linear auto-regressive models. Our proof yields nearly…
Learning to Control Linear Systems can be Hard
Anastasios Tsiamis, Ingvar Ziemann, Manfred Morari +2
In this paper, we study the statistical difficulty of learning to control linear systems. We focus on two standard benchmarks, the sample complexity of stabilization, and the regre…
Single Trajectory Nonparametric Learning of Nonlinear Dynamics
Ingvar Ziemann, Henrik Sandberg, Nikolai Matni
Given a single trajectory of a dynamical system, we analyze the performance of the nonparametric least squares estimator (LSE). More precisely, we give nonasymptotic expected …
Noninvasively improving the orbit-response matrix while continuously correcting the orbit
Ingvar Ziemann, Volker Ziemann
Based on continuously recorded beam positions and corrector excitations from, for example, a closed-orbit feedback system we describe an algorithm that continuously updates an esti…
On Uninformative Optimal Policies in Adaptive LQR with Unknown B-Matrix
Ingvar Ziemann, Henrik Sandberg
This paper presents local asymptotic minimax regret lower bounds for adaptive Linear Quadratic Regulators (LQR). We consider affinely parametrized -matrices and known -matric…