publications

2023

1. Dependence and Model Selection in LLP: The Problem of Variants

   Gabriel Franco, Mark Crovella, and Giovanni Comarela

   In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

   Abs

   The problem of Learning from Label Proportions (LLP) has received considerable research attention and has numerous practical applications. In LLP, a hypothesis assigning labels to items is learned using knowledge of only the proportion of labels found in predefined groups, called bags. While a number of algorithmic approaches to learning in this context have been proposed, very little work has addressed the model selection problem for LLP. Nonetheless, it is not obvious how to extend straightforward model selection approaches to LLP, in part because of the lack of item labels. More fundamentally, we argue that a careful approach to model selection for LLP requires consideration of the dependence structure that exists between bags, items, and labels. In this paper we formalize this structure and show how it affects model selection. We show how this leads to improved methods of model selection that we demonstrate outperform the state of the art over a wide range of datasets and LLP algorithms.

2. Evaluating LLP Methods: Challenges and Approaches

   Gabriel Franco, Giovanni Comarela, and Mark Crovella

   arXiv preprint arXiv:2310.19065, 2023

2019

1. Towards an Interpretable Metric for DOTA 2 Players: An Unsupervised Learning Approach

   Gabriel Franco, Marcos Henrique Fonseca Ribeiro, and Giovanni Comarela

   In 2019 8th Brazilian Conference on Intelligent Systems (BRACIS), 2019

2016

1. Plasmodium Falciparum Infection: In Silico Preliminary Studies

   Andréia Patricia Gomes, Brenda Silveira Valles Moreira, Felipe José Dutra Dias, and 8 more authors

   Abakós, 2016