Search Results for author:
Found 7 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.
Partial Rankings of Optimizers
We introduce a framework for benchmarking optimizers according to multiple criteria over various test functions.
Pseudo Label Selection is a Decision Problem
At its heart is a novel selection criterion: an analytical approximation of the posterior predictive of pseudo-samples and labeled data.
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.
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.
