6 citations · 13 across the 7 of their papers we have counts for
7 papers
Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures
João Bravo, Jacopo Bono, Pedro Saleiro +2
Systems characterized by evolving interactions, prevalent in social, financial, and biological domains, are effectively modeled as continuous-time dynamic graphs (CTDGs). To manage…
NRGBoost: Energy-Based Generative Boosted Trees
João Bravo
Despite the rise to dominance of deep learning in unstructured data domains, tree-based methods such as Random Forests (RF) and Gradient Boosted Decision Trees (GBDT) are still the…
RIFF: Inducing Rules for Fraud Detection from Decision Trees
João Lucas Martins, João Bravo, Ana Sofia Gomes +2
Financial fraud is the cause of multi-billion dollar losses annually. Traditionally, fraud detection systems rely on rules due to their transparency and interpretability, key featu…
Adversarial training for tabular data with attack propagation
Tiago Leon Melo, João Bravo, Marco O. P. Sampaio +4
Adversarial attacks are a major concern in security-centered applications, where malicious actors continuously try to mislead Machine Learning (ML) models into wrongly classifying…
FairGBM: Gradient Boosting with Fairness Constraints
André F Cruz, Catarina Belém, Sérgio Jesus +3
Tabular data is prevalent in many high-stakes domains, such as financial services or public policy. Gradient Boosted Decision Trees (GBDT) are popular in these settings due to thei…
Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions
José Pombal, André F. Cruz, João Bravo +3
In recent years, machine learning algorithms have become ubiquitous in a multitude of high-stakes decision-making applications. The unparalleled ability of machine learning algorit…
ARMS: Automated rules management system for fraud detection
David Aparício, Ricardo Barata, João Bravo +2
Fraud detection is essential in financial services, with the potential of greatly reducing criminal activities and saving considerable resources for businesses and customers. We ad…