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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 Ãlvarez, 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 Ãlvarez, 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…
Supervised Learning with Evolving Tasks and Performance Guarantees
Verónica Ãlvarez, Santiago Mazuelas, Jose A. Lozano
Multiple supervised learning scenarios are composed by a sequence of classification tasks. For instance, multi-task learning and continual learning aim to learn a sequence of tasks…