13 citations · 18 across the 11 of their papers we have counts for
11 papers
Efficient Large-Scale Learning of Minimax Risk Classifiers
Kartheek Bondugula, Santiago Mazuelas, Aritz Pérez
Supervised learning with large-scale data usually leads to complex optimization problems, especially for classification tasks with multiple classes. Stochastic subgradient methods…
A Review on Single-Problem Multi-Attempt Heuristic Optimization
Judith Echevarrieta, Etor Arza, Aritz Pérez +1
In certain real-world optimization scenarios, practitioners are not interested in solving multiple problems but rather in finding the best solution to a single, specific problem. W…
Risk-based Calibration for Generative Classifiers
Aritz Pérez, Carlos Echegoyen, Guzmán Santafé
Generative classifiers are constructed on the basis of a joint probability distribution and are typically learned using closed-form procedures that rely on data statistics and maxi…
Time-dependent Probabilistic Generative Models for Disease Progression
Onintze Zaballa, Aritz Pérez, Elisa Gómez-Inhiesto +2
Electronic health records contain valuable information for monitoring patients' health trajectories over time. Disease progression models have been developed to understand the unde…
Speeding-up Evolutionary Algorithms to solve Black-Box Optimization Problems
Judith Echevarrieta, Etor Arza, Aritz Pérez
Population-based evolutionary algorithms are often considered when approaching computationally expensive black-box optimization problems. They employ a selection mechanism to choos…
Dirichlet process mixture models for non-stationary data streams
Ioar Casado, Aritz Pérez
In recent years, we have seen a handful of work on inference algorithms over non-stationary data streams. Given their flexibility, Bayesian non-parametric models are a good candida…