90 citations · 174 across the 35 of their papers we have counts for
45 papers
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…
Decoupling Inference from State Updates in Low-Latency Feature Engines via Probabilistic Thinning
Augusto Peres, Iker Perez, Pedro Valdeira +4
Streaming data systems increasingly underpin Machine Learning workflows that maintain large numbers of continuously updated aggregations. In production settings, each incoming even…
Rethinking XAI Evaluation: A Human-Centered Audit of Shapley Benchmarks in High-Stakes Settings
Inês Oliveira e Silva, Sérgio Jesus, Iker Perez +4
Shapley values are a cornerstone of explainable AI, yet their proliferation into competing formulations has created a fragmented landscape with little consensus on practical deploy…
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…
Evaluating Transfer Learning Methods on Real-World Data Streams: A Case Study in Financial Fraud Detection
Ricardo Ribeiro Pereira, Jacopo Bono, Hugo Ferreira +3
When the available data for a target domain is limited, transfer learning (TL) methods can be used to develop models on related data-rich domains, before deploying them on the targ…