3 papers
cs.LG2025
Divide, Specialize, and Route: A New Approach to Efficient Ensemble Learning
Jakub Piwko, Jędrzej Ruciński, Dawid Płudowski +5
Ensemble learning has proven effective in boosting predictive performance, but traditional methods such as bagging, boosting, and dynamic ensemble selection (DES) suffer from high…
cs.LG2024
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making
Anna Kozak, Dominik Kędzierski, Jakub Piwko +2
In many applications, model ensembling proves to be better than a single predictive model. Hence, it is the most common post-processing technique in Automated Machine Learning (Aut…
cs.LG2024
Rethinking of Encoder-based Warm-start Methods in Hyperparameter Optimization
Dawid Płudowski, Antoni Zajko, Anna Kozak +1
Effectively representing heterogeneous tabular datasets for meta-learning purposes remains an open problem. Previous approaches rely on predefined meta-features, for example, stati…