111 citations · 114 across the 8 of their papers we have counts for
9 papers
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…
Supervised Learning with Evolving Tasks and Performance Guarantees
Verónica Álvarez, 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…
Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees
Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano
For a sequence of classification tasks that arrive over time, it is common that tasks are evolving in the sense that consecutive tasks often have a higher similarity. The increment…
Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees
Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano
The statistical characteristics of instance-label pairs often change with time in practical scenarios of supervised classification. Conventional learning techniques adapt to such c…
MRCpy: A Library for Minimax Risk Classifiers
Kartheek Bondugula, Verónica Álvarez, José I. Segovia-Martín +2
Libraries for supervised classification have enabled the wide-spread usage of machine learning methods. Existing libraries, such as scikit-learn, caret, and mlpack, implement techn…
Probabilistic Load Forecasting Based on Adaptive Online Learning
Verónica Álvarez, Santiago Mazuelas, José A. Lozano
Load forecasting is crucial for multiple energy management tasks such as scheduling generation capacity, planning supply and demand, and minimizing energy trade costs. Such relevan…