1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.NE2025
Adaptive Estimation of the Number of Algorithm Runs in Stochastic Optimization
Tome Eftimov, Peter Korošec
Determining the number of algorithm runs is a critical aspect of experimental design, as it directly influences the experiment's duration and the reliability of its outcomes. This…
cs.LG2023★ 1 cited
PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization
Ana Kostovska, Gjorgjina Cenikj, Diederick Vermetten +6
The performance of automated algorithm selection (AAS) strongly depends on the portfolio of algorithms to choose from. Selecting the portfolio is a non-trivial task that requires b…
cs.LG2023
Sensitivity Analysis of RF+clust for Leave-one-problem-out Performance Prediction
Ana Nikolikj, Michal Pluháček, Carola Doerr +2
Leave-one-problem-out (LOPO) performance prediction requires machine learning (ML) models to extrapolate algorithms' performance from a set of training problems to a previously uns…