1 citations · 1 across the 5 of their papers we have counts for
5 papers
Finding Low Star Discrepancy 3D Kronecker Point Sets Using Algorithm Configuration Techniques
Imène Ait Abderrahim, Carola Doerr, Martin Durand
The L infinity star discrepancy is a measure for how uniformly a point set is distributed in a given space. Point sets of low star discrepancy are used as designs of experiments, a…
Optimizing Kernel Discrepancies via Subset Selection
Deyao Chen, François Clément, Carola Doerr +1
Kernel discrepancies are a powerful tool for analyzing worst-case errors in quasi-Monte Carlo (QMC) methods. Building on recent advances in optimizing such discrepancy measures, we…
carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks
Carolin Benjamins, Helena Graf, Sarah Segel +14
Hyperparameter Optimization (HPO) is crucial to develop well-performing machine learning models. In order to ease prototyping and benchmarking of HPO methods, we propose carps, a b…
Transforming the Challenge of Constructing Low-Discrepancy Point Sets into a Permutation Selection Problem
François Clément, Carola Doerr, Kathrin Klamroth +1
Low discrepancy point sets have been widely used as a tool to approximate continuous objects by discrete ones in numerical processes, for example in numerical integration. Followin…
Constructing Optimal Star Discrepancy Sets
François Clément, Carola Doerr, Kathrin Klamroth +1
The star discrepancy is a very well-studied measure used to quantify the uniformity of a point set distribution. Constructing optimal point sets for this measure is se…