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Found 9 papers, 3 papers with code
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration
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.
Evaluating machine learning models in non-standard settings: An overview and new findings
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.
Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement
Spaces with locally varying scale of measurement, like multidimensional structures with differently scaled dimensions, are pretty common in statistics and machine learning.
In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised Learning
As a practical proof of concept, we spotlight the application of three of our robust extensions on simulated and real-world data.
Approximately Bayes-Optimal Pseudo Label Selection
We derive this selection criterion by proving Bayes optimality of the posterior predictive of pseudo-samples.
Multi-Target Decision Making under Conditions of Severe Uncertainty
The quality of consequences in a decision making problem under (severe) uncertainty must often be compared among different targets (goals, objectives) simultaneously.
Statistical Comparisons of Classifiers by Generalized Stochastic Dominance
This yields indeed a powerful framework for the statistical comparison of classifiers over multiple data sets with respect to multiple quality criteria simultaneously.
Accounting for Gaussian Process Imprecision in Bayesian Optimization
In this paper, we propose Prior-mean-RObust Bayesian Optimization (PROBO) that outperforms classical BO on specific problems.
Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty
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.
