2 citations · 9 across the 8 of their papers we have counts for
4 papers · 1 filter
Learning Relevant Contextual Variables Within Bayesian Optimization
Julien Martinelli, Ayush Bharti, Armi Tiihonen +5
Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding…
Multi-Fidelity Bayesian Optimization with Unreliable Information Sources
Petrus Mikkola, Julien Martinelli, Louis Filstroff +1
Bayesian optimization (BO) is a powerful framework for optimizing black-box, expensive-to-evaluate functions. Over the past decade, many algorithms have been proposed to integrate…
Bayesian Optimization Augmented with Actively Elicited Expert Knowledge
Daolang Huang, Louis Filstroff, Petrus Mikkola +2
Bayesian optimization (BO) is a well-established method to optimize black-box functions whose direct evaluations are costly. In this paper, we tackle the problem of incorporating e…
A Ranking Model Motivated by Nonnegative Matrix Factorization with Applications to Tennis Tournaments
Rui Xia, Vincent Y. F. Tan, Louis Filstroff +1
We propose a novel ranking model that combines the Bradley-Terry-Luce probability model with a nonnegative matrix factorization framework to model and uncover the presence of laten…