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How Far Out of Distribution Can We Go With ELA Features and Still Be Able to Rank Algorithms?, and . SSCI, page 341-346. IEEE, (2023)PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization., , , , , , , , and . AutoML, volume 224 of Proceedings of Machine Learning Research, page 11/1-17. PMLR, (2023)Assessing the Generalizability of a Performance Predictive Model., , , , , , , , and . GECCO Companion, page 311-314. ACM, (2023)SELECTOR: selecting a representative benchmark suite for reproducible statistical comparison., , , , , and . GECCO, page 620-629. ACM, (2022)CafeteriaSA corpus: scientific abstracts annotated across different food semantic resources., , , , , , , , and . Database J. Biol. Databases Curation, (2022)BuTTER: BidirecTional LSTM for Food Named-Entity Recognition., , , , and . IEEE BigData, page 3550-3556. IEEE, (2020)Learned Features vs. Classical ELA on Affine BBOB Functions., , , , and . PPSN (2), volume 15149 of Lecture Notes in Computer Science, page 137-153. Springer, (2024)Towards understanding the importance of time-series features in automated algorithm performance prediction., , and . Expert Syst. Appl., 213 (Part): 119023 (2023)The Pitfalls of Benchmarking in Algorithm Selection: What We Are Getting Wrong., and . GECCO, page 1181-1189. ACM, (2025)FoodBench: A Domain-Targeted Question-Answering Dataset for Food and Nutrition Applications., , , , , , , and . (December 2025)