13 citations · 14 across the 9 of their papers we have counts for
9 papers · 1 filter
Transfer Learning for Evolving Domains
Ricardo Ribeiro Pereira, Jacopo Bono, Hugo Ferreira +4
Transfer learning explores how to leverage knowledge from various tasks or domains (sources) to enhance predictive performance in related tasks or domains (targets). Typically, tra…
Causal Discovery on Irregular Time Series
Martim Penim, Ricardo Ribeiro Pereira, Jacopo Bono +3
Causal discovery methods have shown strong performance in temporal systems, but they typically rely on regular and discrete lag structures, limiting their applicability to regularl…
MUSE: Multi-Tenant Model Serving With Seamless Model Updates
Cláudio Correia, Alberto E. A. Ferreira, Lucas Martins +7
In binary classification systems, decision thresholds translate model scores into actions. Choosing suitable thresholds relies on the specific distribution of the underlying model…
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…
Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs
Ahmad Naser Eddin, Jacopo Bono, David Aparício +3
Continuous-time dynamic graphs (CTDGs) are essential for modeling interconnected, evolving systems. Traditional methods for extracting knowledge from these graphs often depend on f…
DiConStruct: Causal Concept-based Explanations through Black-Box Distillation
Ricardo Moreira, Jacopo Bono, Mário Cardoso +3
Model interpretability plays a central role in human-AI decision-making systems. Ideally, explanations should be expressed using human-interpretable semantic concepts. Moreover, th…
The GANfather: Controllable generation of malicious activity to improve defence systems
Ricardo Ribeiro Pereira, Jacopo Bono, João Tiago Ascensão +3
Machine learning methods to aid defence systems in detecting malicious activity typically rely on labelled data. In some domains, such labelled data is unavailable or incomplete. I…
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs
Ahmad Naser Eddin, Jacopo Bono, David Aparício +4
Many real-world datasets have an underlying dynamic graph structure, where entities and their interactions evolve over time. Machine learning models should consider these dynamics…
Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs
Ahmad Naser Eddin, Jacopo Bono, David Aparício +4
Money laundering is a global problem that concerns legitimizing proceeds from serious felonies (1.7-4 trillion euros annually) such as drug dealing, human trafficking, or corruptio…