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Found 8 papers, 0 papers with code
Frequency-domain Gaussian Process Models for $H_\infty$ Uncertainties
We then establish how an $H_\infty$ Gaussian process can serve as a prior for Bayesian system identification and as a probabilistic uncertainty model for probabilistic robust control.
Frequency Domain Gaussian Process Models for $H^\infty$ Uncertainties
Complex-valued Gaussian processes are used in Bayesian frequency-domain system identification as prior models for regression.
Machine Learning for Smart and Energy-Efficient Buildings
In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient.
Data-Driven Reachability analysis and Support set Estimation with Christoffel Functions
In addition to applying classical Vapnik-Chervonenkis (VC) dimension bound arguments, we apply the PAC-Bayes theorem by leveraging a formal connection between kernelized empirical inverse Christoffel functions and Gaussian process regression models.
DaDRA: A Python Library for Data-Driven Reachability Analysis
Reachability analysis is used to determine all possible states that a system acting under uncertainty may reach.
Symbolic Abstractions From Data: A PAC Learning Approach
Symbolic control techniques aim to satisfy complex logic specifications.
Data-Driven Reachability Analysis with Christoffel Functions
We present an algorithm for data-driven reachability analysis that estimates finite-horizon forward reachable sets for general nonlinear systems using level sets of a certain class of polynomials known as Christoffel functions.
Estimating Reachable Sets with Scenario Optimization
Many practical systems are not amenable to the reachability methods that give guarantees of correctness, since they have dynamics that are strongly nonlinear, uncertain, and possibly unknown.
