7 papers
Nearly Instance-Optimal Parameter Recovery from Many Trajectories via Hellinger Localization
Eliot Shekhtman, Yichen Zhou, Ingvar Ziemann +2
Learning from temporally-correlated data is a core facet of modern machine learning. Yet our understanding of sequential learning remains incomplete, particularly in the multi-traj…
Finite Sample Analysis of Open-loop Subspace Identification Methods
Jiabao He, Ingvar Ziemann, Cristian R. Rojas +2
Subspace identification methods (SIMs) are known for their simple parameterization for MIMO systems and robust numerical properties. However, a comprehensive statistical analysis o…
An Elementary Proof of the Hanson-Wright Inequality
Ingvar Ziemann
The Hanson-Wright inequality establishes exponential concentration for quadratic forms , where is a vector with independent sub-Gaussian entries and with parameters de…
Nonconvex Linear System Identification with Minimal State Representation
Uday Kiran Reddy Tadipatri, Benjamin D. Haeffele, Joshua Agterberg +2
Low-order linear System IDentification (SysID) addresses the challenge of estimating the parameters of a linear dynamical system from finite samples of observations and control inp…
Logarithmic Regret for Nonlinear Control
James Wang, Bruce D. Lee, Ingvar Ziemann +1
We address the problem of learning to control an unknown nonlinear dynamical system through sequential interactions. Motivated by high-stakes applications in which mistakes can be…
Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss
Ingvar Ziemann, Stephen Tu, George J. Pappas +1
In this work, we study statistical learning with dependent (-mixing) data and square loss in a hypothesis class where is the norm $\|f\|_{Î…