Automating Machine Learning Pipeline Design via Metalearning
Alcobaça, E., & de Carvalho, A. C. (2026). Automating Machine Learning Pipeline Design via Metalearning. Concurso de Teses e Dissertações da SBC (CTD-SBC), 21-30.
Publications
Building Customer Support AI Agents at 100M-User Scale: An Evaluation-Driven Framework
Gupta, A., Rossell, K., Alcobaça, E., Pacheco, J. C. L., de Lima, C. B., Tang, S., Rabachini, L. P., Moneda, L., Fei, H., Silva, D., & Ramanath, R. (2026). Building Customer Support AI Agents at 100M-User Scale: An Evaluation-Driven Framework. KDD 2026. arXiv:2606.08867.
Exploring One Million Machine Learning Pipelines: A Benchmarking Study
Alcobaça, E., & de Carvalho, A. C. P. L. F. (2025). Exploring One Million Machine Learning Pipelines: A Benchmarking Study. In Proceedings of the Fourth International Conference on Automated Machine Learning, PMLR 293:22/1-34.
Dynamic design of machine learning pipelines via metalearning
Alcobaça, E., & de Carvalho, A. C. (2025). Dynamic design of machine learning pipelines via metalearning. arXiv preprint arXiv:2508.13436.
A literature review on automated machine learning
Alcobaça, E., & de Carvalho, A. C. (2025). A literature review on automated machine learning. Artificial Intelligence Review, 59(1), 5.
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
El Baz, A., Ullah, I., Alcobaça, E., Carvalho, A. C., Chen, H., Ferreira, F., Gouk, H., et al. (2022). Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification. NeurIPS 2021 Competitions and Demonstrations Track, 80-96.
Machine learning unveils composition-property relationships in chalcogenide glasses
Mastelini, S. M., Cassar, D. R., Alcobaça, E., Botari, T., de Carvalho, A. C., & Zanotto, E. D. (2022). Machine learning unveils composition-property relationships in chalcogenide glasses. Acta Materialia, 240, 118302.
Predicting and interpreting oxide glass properties by machine learning using large datasets
Cassar, D. R., Mastelini, S. M., Botari, T., Alcobaça, E., de Carvalho, A. C., & Zanotto, E. D. (2021). Predicting and interpreting oxide glass properties by machine learning using large datasets. Ceramics International.
Boosting meta-learning with simulated data complexity measures
Garcia, L. P., Rivolli, A., Alcobaça, E., Lorena, A. C., & de Carvalho, A. C. (2020). Boosting meta-learning with simulated data complexity measures. Intelligent Data Analysis, 24(5), 1011-1028.
Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters
Mantovani, R. G., Rossi, A. L. D., Alcobaça, E., Gertrudes, J. C., Junior, S. B., & de Carvalho, A. C. P. D. L. F. (2020). Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters. arXiv preprint arXiv:2008.00025.
MFE: Towards reproducible meta-feature extraction
Alcobaça, E., Siqueira, F., Rivolli, A., Garcia, L. P. F., Oliva, J. T., & de Carvalho, A. C. (2020). MFE: Towards reproducible meta-feature extraction. Journal of Machine Learning Research , 21, 111-1.
Explainable machine learning algorithms for predicting glass transition temperatures
Alcobaça, E., Mastelini, S. M., Botari, T., Pimentel, B. A., Cassar, D. R., de Leon Ferreira, A. C. P., & Zanotto, E. D. (2020). Explainable machine learning algorithms for predicting glass transition temperatures. Acta Materialia
Transfer learning for algorithm recommendation
Pereira, G. T., Santos, M. D., Alcobaça, E., Mantovani, R., & Carvalho, A. (2019). Transfer learning for algorithm recommendation. arXiv preprint arXiv:1910.07012.
A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers
Mantovani, R. G., Rossi, A. L., Alcobaça, E., Vanschoren, J., & de Carvalho, A. C. (2019). A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers. Information Sciences, 501, 193-221.
Dimensionality reduction for the algorithm recommendation problem
Alcobaça, E., Mantovani, R. G., Rossi, A. L., & De Carvalho, A. C. (2018, October). Dimensionality reduction for the algorithm recommendation problem. In 2018 7th Brazilian Conference on Intelligent Systems (BRACIS) (pp. 318-323). IEEE.