Search Results for author:
Found 5 papers, 3 papers with code
Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits
We design a near-optimal algorithm whose sample complexity matches the lower bound, up to log factors.
Learning Linear Dynamical Systems with Semi-Parametric Least Squares
We analyze a simple prefiltered variation of the least squares estimator for the problem of estimation with biased, semi-parametric noise, an error model studied more broadly in causal statistics and active learning.
Minimax Lower Bounds for $\mathcal{H}_\infty$-Norm Estimation
The problem of estimating the $\mathcal{H}_\infty$-norm of an LTI system from noisy input/output measurements has attracted recent attention as an alternative to parameter identification for bounding unmodeled dynamics in robust control.
Finite-Data Performance Guarantees for the Output-Feedback Control of an Unknown System
As the systems we control become more complex, first-principle modeling becomes either impossible or intractable, motivating the use of machine learning techniques for the control of systems with continuous action spaces.
Non-Asymptotic Analysis of Robust Control from Coarse-Grained Identification
We derive bounds on the number of noisy input/output samples from a stable linear time-invariant system that are sufficient to guarantee that the corresponding finite impulse response approximation is close to the true system in the $\mathcal{H}_\infty$-norm.
