all AI news
Estimation Sample Complexity of a Class of Nonlinear Continuous-time Systems
April 24, 2024, 4:46 a.m. | Simon Kuang, Xinfan Lin
stat.ML updates on arXiv.org arxiv.org
Abstract: We present a method of parameter estimation for large class of nonlinear systems, namely those in which the state consists of output derivatives and the flow is linear in the parameter. The method, which solves for the unknown parameter by directly inverting the dynamics using regularized linear regression, is based on new design and analysis ideas for differentiation filtering and regularized least squares. Combined in series, they yield a novel finite-sample bound on mean absolute …
abstract arxiv class complexity continuous cs.sy derivatives dynamics eess.sy flow linear math.oc sample state stat.ml systems the unknown type
More from arxiv.org / stat.ML updates on arXiv.org
Learning linear dynamical systems under convex constraints
2 days, 22 hours ago |
arxiv.org
Inverse Unscented Kalman Filter
3 days, 22 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Machine Learning Engineer
@ Apple | Sunnyvale, California, United States