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 • 23 Oct 2023 • Roman Hornung, Malte Nalenz, Lennart Schneider, Andreas Bender, Ludwig Bothmann, Bernd Bischl, Thomas Augustin, Anne-Laure Boulesteix
Our findings corroborate the concern that standard resampling methods often yield biased GE estimates in non-standard settings, underscoring the importance of tailored GE estimation.
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.
no code implementations • 13 Dec 2022 • Christoph Jansen, Georg Schollmeyer, Thomas Augustin
The quality of consequences in a decision making problem under (severe) uncertainty must often be compared among different targets (goals, objectives) simultaneously.
no code implementations • 5 Sep 2022 • Christoph Jansen, Malte Nalenz, Georg Schollmeyer, Thomas Augustin
This yields indeed a powerful framework for the statistical comparison of classifiers over multiple data sets with respect to multiple quality criteria simultaneously.
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.
no code implementations • 19 Oct 2021 • Christoph Jansen, Hannah Blocher, Thomas Augustin, Georg Schollmeyer
The first approach directly utilizes the collected ranking data for obtaining the ordinal part of the preferences, while their cardinal part is constructed implicitly by measuring meta data on the decision maker's consideration times.