1 citations · 1 across the 7 of their papers we have counts for
8 papers
Learnability with Partial Labels and Adaptive Nearest Neighbors
Nicolas A. Errandonea, Santiago Mazuelas, Jose A. Lozano +1
Prior work on partial labels learning (PLL) has shown that learning is possible even when each instance is associated with a bag of labels, rather than a single accurate but costly…
Safe Fairness Guarantees Without Demographics in Classification: Spectral Uncertainty Set Perspective
Ainhize Barrainkua, Santiago Mazuelas, Novi Quadrianto +1
As automated classification systems become increasingly prevalent, concerns have emerged over their potential to reinforce and amplify existing societal biases. In the light of thi…
Adaptive Multi-task Learning for Probabilistic Load Forecasting
Onintze Zaballa, Verónica Álvarez, Santiago Mazuelas
Simultaneous load forecasting across multiple entities (e.g., regions, buildings) is crucial for the efficient, reliable, and cost-effective operation of power systems. Accurate lo…
Robust Minimax Boosting with Performance Guarantees
Santiago Mazuelas, Veronica Alvarez
Boosting methods often achieve excellent classification accuracy, but can experience notable performance degradation in the presence of label noise. Existing robust methods for boo…
Reliable Programmatic Weak Supervision with Confidence Intervals for Label Probabilities
Verónica Álvarez, Santiago Mazuelas, Steven An +1
The accurate labeling of datasets is often both costly and time-consuming. Given an unlabeled dataset, programmatic weak supervision obtains probabilistic predictions for the label…
Multi-task Online Learning for Probabilistic Load Forecasting
Onintze Zaballa, Verónica Álvarez, Santiago Mazuelas
Load forecasting is essential for the efficient, reliable, and cost-effective management of power systems. Load forecasting performance can be improved by learning the similarities…