3 papers
cs.LG2024
Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal
Francesco Di Fiore, Michela Nardelli, Laura Mainini
Science and Engineering applications are typically associated with expensive optimization problems to identify optimal design solutions and states of the system of interest. Bayesi…
cs.LG2024
Physics-Aware Multifidelity Bayesian Optimization: a Generalized Formulation
Francesco Di Fiore, Laura Mainini
The adoption of high-fidelity models for many-query optimization problems is majorly limited by the significant computational cost required for their evaluation at every query. Mul…
cs.LG2024
Non-Myopic Multifidelity Bayesian Optimization
Francesco Di Fiore, Laura Mainini
Bayesian optimization is a popular framework for the optimization of black box functions. Multifidelity methods allows to accelerate Bayesian optimization by exploiting low-fidelit…