Dimensionality Reduction for the Algorithm Recommendation Problem. - Edesio Alcobaça; Rafael Gomes Mantovani; André L. D. Rossi; André C. P. L. F. de Carvalho (BRACIS, 2018)

Transfer Learning for Algorithm Recommendation. - Gean Trindade Pereira; Moisés Rocha dos Santos; Edesio Alcobaça; Rafael Gomes Mantovani; André C. P. L. F. de Carvalho (CoRR, 2019)

A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers. - Rafael Gomes Mantovani; André L. D. Rossi; Edesio Alcobaça; Joaquin Vanschoren; André C. P. L. F. de Carvalho (Inf. Sci., 2019)

Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters. - Rafael Gomes Mantovani; André Luis Debiaso Rossi; Edesio Alcobaça; Jadson Castro Gertrudes; Sylvio Barbon Junior; André Carlos Ponce de Leon Ferreira de Carvalho (CoRR, 2020)

Boosting meta-learning with simulated data complexity measures. - Luís Paulo F. Garcia; Adriano Rivolli; Edesio Alcobaça; Ana Carolina Lorena; André C. P. L. F. de Carvalho (Intell. Data Anal., 2020)

MFE: Towards reproducible meta-feature extraction. - Edesio Alcobaça; Felipe Siqueira; Adriano Rivolli; Luís Paulo F. Garcia; Jefferson Tales Oliva; André C. P. L. F. de Carvalho (J. Mach. Learn. Res., 2020)

Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification. - Adrian El Baz; Ihsan Ullah; Edesio Alcobaça; André C. P. L. F. de Carvalho; Hong Chen 011; Fabio Ferreira; Henry Gouk; Chaoyu Guan; Isabelle Guyon; Timothy M. Hospedales; Shell Hu; Mike Huisman; et al. (NeurIPS (Competition and Demos), 2021)

Dynamic Design of Machine Learning Pipelines via Metalearning. - Edesio Alcobaça; André C. P. L. F. de Carvalho (CoRR, 2025)

A literature review on automated machine learning. - Edesio Alcobaça; André C. P. L. F. de Carvalho (Artif. Intell. Rev., 2026)