Arthur Colombini Gusmão, Alvaro Henrique Chaim Correia, Glauber De Bona, and Fabio Gagliardi Cozman.
Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach.
Proceedings of the ICML Workshop on Human Interpretability in Machine Learning, pp. 79-86, 2018. Link. Slides. Presentation.

Andrey Ruschel, Arthur Colombini Gusmão, Fabio Gagliardi Cozman.
Explaining Answers Generated by Knowledge Graph Embeddings.
International Journal of Approximate Reasoning, vol. 171, art. 109183, 2024. Link.

Francisco H.O. Vieira de Faria, Arthur Colombini Gusmão, Glauber De Bona, Denis Deratani Mauá, Fabio Gagliardi Cozman.
Speeding Up Parameter and Rule Learning for Acyclic Probabilistic Logic Programs.
International Journal of Approximate Reasoning, vol. 106, pp. 32-50, 2019. Preprint link.

Arthur Colombini Gusmão.
Interpreting Embedding Models of Knowledge Bases.
(Master’s thesis) Universidade de São Paulo, 2018. Link.

Andrey Ruschel, Arthur Colombini Gusmão, Gustavo Padilha Polleti, and Fabio Gagliardi Cozman.
Explaining Completions Produced by Embeddings of Knowledge Graphs.
European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp. 324-335, 2019. Preprint link.

Forough Yaghoubi, Armin Catovic, Arthur Colombini Gusmão, Jan Pieczkowski, Peter Boros.
Traffic Flow Estimation using Machine Learning and 4G/5G Radio Frequency Counters.
IEEE 95th Vehicular Technology Conference (VTC2022-Spring), pp. 1-5, 2022. Link.

Francisco H. O. Vieira de Faria, Arthur C. Gusmao, Glauber De Bona, Denis D. Maua, and Fabio G. Cozman.
Parameter Learning in ProbLog with Probabilistic Rules.
Symposium on Knowledge Discovery, Mining and Learning (KDMILE), pp. 27-34, 2017. Preprint link.

Francisco H. O. Vieira de Faria, Arthur C. Gusmao, Fabio G. Cozman, and Denis D. Maua.
Speeding-up ProbLog’s Parameter Learning (Extended Abstract).
International Workshop on Statistical Relational AI (StarAI), 2017. Link.