13 citations · 30 across the 15 of their papers we have counts for
15 papers
Geometric Learning in Black-Box Optimization: A GNN Framework for Algorithm Performance Prediction
Ana Kostovska, Carola Doerr, Sašo Džeroski +2
Automated algorithm performance prediction in numerical blackbox optimization often relies on problem characterizations, such as exploratory landscape analysis features. These feat…
Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks
Ana Nikolikj, Ana Kostovska, Gjorgjina Cenikj +2
This study examines the generalization ability of algorithm performance prediction models across various benchmark suites. Comparing the statistical similarity between the problem…
Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks
Ana Nikolikj, Ana Kostovska, Diederick Vermetten +2
This study explores the influence of modules on the performance of modular optimization frameworks for continuous single-objective black-box optimization. There is an extensive var…
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…
Comparing Algorithm Selection Approaches on Black-Box Optimization Problems
Ana Kostovska, Anja Jankovic, Diederick Vermetten +3
Performance complementarity of solvers available to tackle black-box optimization problems gives rise to the important task of algorithm selection (AS). Automated AS approaches can…
Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms
Ana Kostovska, Diederick Vermetten, Sašo Džeroski +3
Empirical data plays an important role in evolutionary computation research. To make better use of the available data, ontologies have been proposed in the literature to organize t…
FAIRification of MLC data
Ana Kostovska, Jasmin Bogatinovski, Andrej Treven +3
The multi-label classification (MLC) task has increasingly been receiving interest from the machine learning (ML) community, as evidenced by the growing number of papers and method…
OPTION: OPTImization Algorithm Benchmarking ONtology
Ana Kostovska, Diederick Vermetten, Carola Doerr +3
Many optimization algorithm benchmarking platforms allow users to share their experimental data to promote reproducible and reusable research. However, different platforms use diff…
Explainable Model-specific Algorithm Selection for Multi-Label Classification
Ana Kostovska, Carola Doerr, Sašo Džeroski +3
Multi-label classification (MLC) is an ML task of predictive modeling in which a data instance can simultaneously belong to multiple classes. MLC is increasingly gaining interest i…
Discover the Mysteries of the Maya: Selected Contributions from the Machine Learning Challenge & The Discovery Challenge Workshop at ECML PKDD 2021
Dragi Kocev, Nikola Simidjievski, Ana Kostovska +2
The volume contains selected contributions from the Machine Learning Challenge "Discover the Mysteries of the Maya", presented at the Discovery Challenge Track of The European Conf…
The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants
Ana Kostovska, Diederick Vermetten, Sašo Džeroski +3
Selecting the most suitable algorithm and determining its hyperparameters for a given optimization problem is a challenging task. Accurately predicting how well a certain algorithm…
Per-run Algorithm Selection with Warm-starting using Trajectory-based Features
Ana Kostovska, Anja Jankovic, Diederick Vermetten +4
Per-instance algorithm selection seeks to recommend, for a given problem instance and a given performance criterion, one or several suitable algorithms that are expected to perform…
Trajectory-based Algorithm Selection with Warm-starting
Anja Jankovic, Diederick Vermetten, Ana Kostovska +3
Landscape-aware algorithm selection approaches have so far mostly been relying on landscape feature extraction as a preprocessing step, independent of the execution of optimization…
GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data
Ana Kostovska, Matej Petković, Tomaž Stepišnik +8
We present GalaxAI - a versatile machine learning toolbox for efficient and interpretable end-to-end analysis of spacecraft telemetry data. GalaxAI employs various machine learning…
OPTION: OPTImization Algorithm Benchmarking ONtology
Ana Kostovska, Diederick Vermetten, Carola Doerr +3
Many platforms for benchmarking optimization algorithms offer users the possibility of sharing their experimental data with the purpose of promoting reproducible and reusable resea…