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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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Talk 1 on Relevant Topic in Your Field

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Tutorial 1 on Relevant Topic in Your Field

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Talk 2 on Relevant Topic in Your Field

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Conference Proceeding talk 3 on Relevant Topic in Your Field

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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