Abstract Submission

6650. Sparse RBF Regression for the Optimization of Noisy Expensive Functions

Contributed abstract in session FA-5: Optimization and Artificial Intelligence II, stream Optimization and Artificial Intelligence.

Friday, 9:00 - 10:40
Room: Pontryagin

Authors (first author is the speaker)

1. Alessio Sortino
Department of Information Engineering, University of Florence
2. Matteo Lapucci
Department of Information Engineering, University of Florence
3. Fabio Schoen
Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Firenze


Global optimization problems for black-box functions are usually addressed by building a surrogate model over the data and an acquisition function to decide where to place the next observation. When data are noisy the surrogate should not trust the latter too much. This typically introduces an extra hyperparameter into the model that corresponds to the variance of the noise. In this work we present a novel approach where a robust RBF-based surrogate model is built from the solution of a particular MIQP problem. Experimental results show the effectiveness of our approach w.r.t. existent methods


Status: accepted

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