no code implementations • 7 Mar 2024 • Julian Rodemann, Federico Croppi, Philipp Arens, Yusuf Sale, Julia Herbinger, Bernd Bischl, Eyke Hüllermeier, Thomas Augustin, Conor J. Walsh, Giuseppe Casalicchio
We address this issue by proposing ShapleyBO, a framework for interpreting BO's proposals by game-theoretic Shapley values. They quantify each parameter's contribution to BO's acquisition function.
no code implementations • 26 Feb 2024 • Julian Rodemann, Hannah Blocher
We introduce a framework for benchmarking optimizers according to multiple criteria over various test functions.
no code implementations • 25 Sep 2023 • Julian Rodemann
At its heart is a novel selection criterion: an analytical approximation of the posterior predictive of pseudo-samples and labeled data.
1 code implementation • 22 Jun 2023 • Christoph Jansen, Georg Schollmeyer, Hannah Blocher, Julian Rodemann, Thomas Augustin
Spaces with locally varying scale of measurement, like multidimensional structures with differently scaled dimensions, are pretty common in statistics and machine learning.
1 code implementation • 2 Mar 2023 • Julian Rodemann, Christoph Jansen, Georg Schollmeyer, Thomas Augustin
As a practical proof of concept, we spotlight the application of three of our robust extensions on simulated and real-world data.
no code implementations • 17 Feb 2023 • Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin
We derive this selection criterion by proving Bayes optimality of the posterior predictive of pseudo-samples.
1 code implementation • 16 Nov 2021 • Julian Rodemann, Thomas Augustin
In this paper, we propose Prior-mean-RObust Bayesian Optimization (PROBO) that outperforms classical BO on specific problems.