10 citations · 11 across the 2 of their papers we have counts for
3 papers
cs.LG2025
The Pitfalls of Benchmarking in Algorithm Selection: What We Are Getting Wrong
Gašper Petelin, Gjorgjina Cenikj
Algorithm selection, aiming to identify the best algorithm for a given problem, plays a pivotal role in continuous black-box optimization. A common approach involves representing o…
cs.LG2023★ 1 cited
Dealing with zero-inflated data: achieving SOTA with a two-fold machine learning approach
Jože M. Rožanec, Gašper Petelin, João Costa +5
In many cases, a machine learning model must learn to correctly predict a few data points with particular values of interest in a broader range of data where many target values are…
cs.LG2023★ 10 cited
DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems
Gjorgjina Cenikj, Gašper Petelin, Carola Doerr +2
The application of machine learning (ML) models to the analysis of optimization algorithms requires the representation of optimization problems using numerical features. These feat…