82 citations · 247 across the 54 of their papers we have counts for
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Design Choices That Matter: A Functional ANOVA Analysis for Remote Sensing Multi-Label Classification
Maryam Gholami Shiri, Eva Tuba, Sašo Džeroski +2
Benchmarking deep learning (DL) models for multi-label classification (MLC) of remote sensing images (RSI) typically yields rankings that do not generalize beyond the evaluated dat…
Unsupervised Multi-kernel Learning for Automated Algorithm Selection
Yihang Lu, Tome Eftimov, Carola Doerr
Automated algorithm selection in black-box optimization typically relies on supervised models that map landscape features to algorithm performance labels. Such models are costly to…
Evaluating Real-World Generalizability of Algorithm Selection Models
Gjorgjina Cenikj, Jakub Kudela, Eva Tuba +1
Algorithm Selection (AS) aims to automatically identify the most suitable optimization algorithm for a given problem instance by leveraging measurable problem characteristics and h…
Learning to Assess the Reliability of Number-of-Runs Estimation in Stochastic Optimization
Sara Gjorgjieva, Eva Tuba, Tome Eftimov
In large-scale benchmarking of stochastic optimization algorithms, the key challenge is no longer whether repeated runs are needed for reliability, but how to determine when suffic…