4 citations · 8 across the 8 of their papers we have counts for
7 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…
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…
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…
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…