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+34 946 567 842
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+34 946 567 842
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valvarez@bcamath.org
Information of interest
- Orcid: 0000-0001-9775-5888
Veronica received the degree in Mathematics from the University of Salamanca, Spain, in 2019. She is at the Basque Center for Applied Mathematics-BCAM since July 2019 where she is developing data science techniques for energy applications. In particular, she is currently working on probabilistic predictions of energy consumption, generation, and price.
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Supervised Learning with Evolving Tasks and Performance Guarantees
Álvarez, V.
; Mazuelas, S.; Lozano, J.A. (2025-01-09)
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 that is either fixed or grows over time. ...
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Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees
Álvarez, V.; Mazuelas, S.; Lozano, J.A. (2023-12)
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 incremental learning of a growing sequence of ...
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Supervised Learning in Time-dependent Environments with Performance Guarantees
Álvarez, V. (2023-09-25)
In practical scenarios, it is common to learn from a sequence of related problems (tasks). Such tasks are usually time-dependent in the sense that consecutive tasks are often significantly more similar. Time-dependency ...
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Probabilistic Load Forecasting Based on Adaptive Online Learning
Álvarez, V.; Mazuelas, S.; Lozano, J.A. (2020)
Load forecasting is crucial for multiple energy management tasks such as scheduling generation capacity, planning supply and demand, and minimizing energy trade costs. Such relevance has increased even more in recent ...
Probabilistic load forecasting based on adaptive online learning
This repository contains code for the paper Probabilistic Load Forecasting based on Adaptive Online Learning
Authors: Veronica Alvarez
License: free and open source software
Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees (AMRC)
The proposed AMRCs account for multivariate and high-order time changes, provide performance guarantees at specific time instants, and efficiently update classification rules.
Authors: Veronica Alvarez
License: free and open source software
Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees
This repository is the official implementation of Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees.
Authors: Veronica Alvarez
License: free and open source software