과도한-사고로-인한-진실의-이해

제너레이티브-모델-우선의-제로샷-이미지-조화

선형-반복의-보편성과-비선형-투영

die-wahrheit-über-das-überdenken

zero-shot-image-harmonization-with-generative-model-prior-a-human-centric-approach

hiql-offline-goal-conditioned-rl-with-latent-states-as-actions

biomarkers-and-efficacy-of-t-dm1-p-in-her2-breast-cancer-study

universality-of-linear-recurrences-followed-by-non-linear-projections-insights-and-validation


The core message of this work is to provide a finite sample analysis of an active learning algorithm for identifying nonlinear dynamical systems in a control-oriented manner, achieving optimal rates up to logarithmic factors.


coremsg

optimal-active-learning-for-control-oriented-identification-of-nonlinear-dynamical-systems


Optimal Active Learning for Control-Oriented Identification of Nonlinear Dynamical Systems


title_rewrite

Active Learning for Control-Oriented Identification of Nonlinear Systems