3 citations · 4 across the 4 of their papers we have counts for
4 papers
Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints
Jean V. Alves, Diogo Leitão, Sérgio Jesus +5
Learning to defer (L2D) aims to improve human-AI collaboration systems by learning how to defer decisions to humans when they are more likely to be correct than an ML classifier. E…
FiFAR: A Fraud Detection Dataset for Learning to Defer
Jean V. Alves, Diogo Leitão, Sérgio Jesus +4
Public dataset limitations have significantly hindered the development and benchmarking of learning to defer (L2D) algorithms, which aim to optimally combine human and AI capabilit…
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…
Lightweight Automated Feature Monitoring for Data Streams
João Conde, Ricardo Moreira, João Torres +5
Monitoring the behavior of automated real-time stream processing systems has become one of the most relevant problems in real world applications. Such systems have grown in complex…