• Learning a logistic regression with the help of unknown features at prediction stage 

  Martinez, M.; Inza, I.; Lozano, J.A. (2023)

  The use of features available at training time, but not at prediction time, as additional information for training models is known as learning using privileged information paradigm. In this paper, the handling of ...

• Female Models in AI and the Fight Against COVID-19 

  Guerrero, C.; Mazuelas, S. (2022-11-01)

  Gender imbalance has persisted over time and is well documented in science, technology, engineering and mathematics (STEM) and singularly in artificial intelligence (AI). In this article we emphasize the importance of ...

• Efficient Learning of Minimax Risk Classifiers in High Dimensions 

  Bondugula, K.R.; Mazuelas, S.; Pérez, A. (2023-08-01)

  High-dimensional data is common in multiple areas, such as health care and genomics, where the number of features can be tens of thousands. In such scenarios, the large number of features often leads to inefficient ...

• LASSO for streaming data with adaptative filtering 

  Capó, M.; Pérez, A.; Lozano, J.A. (2022)

  Streaming data is ubiquitous in modern machine learning, and so the development of scalable algorithms to analyze this sort of information is a topic of current interest. On the other hand, the problem of l1-penalized ...

• Are the statistical tests the best way to deal with the biomarker selection problem? 

  Urkullu, A.; Pérez, A.; Calvo, B. (2022)

  Statistical tests are a powerful set of tools when applied correctly, but unfortunately the extended misuse of them has caused great concern. Among many other applications, they are used in the detection of biomarkers so ...

• On the use of the descriptive variable for enhancing the aggregation of crowdsourced labels 

  Beñaran-Muñoz, I.; Hernández, J.; Pérez, A. (2022)

  The use of crowdsourcing for annotating data has become a popular and cheap alternative to expert labelling. As a consequence, an aggregation task is required to combine the different labels provided and agree on a single ...

• On the relative value of weak information of supervision for learning generative models: An empirical study 

  Hernández, J.; Pérez, A. (2022)

  Weakly supervised learning is aimed to learn predictive models from partially supervised data, an easy-to-collect alternative to the costly standard full supervision. During the last decade, the research community has ...

• Comparing Two Samples Through Stochastic Dominance: A Graphical Approach 

  Arza, E.; Ceberio, J.; Irurozki, E.; Pérez, A. (2022)

  Nondeterministic measurements are common in real-world scenarios: the performance of a stochastic optimization algorithm or the total reward of a reinforcement learning agent in a chaotic environment are just two examples ...

• Non-parametric discretization for probabilistic labeled data 

  Flores, J.L.; Calvo, B.; Pérez, A. (2022)

  Probabilistic label learning is a challenging task that arises from recent real-world problems within the weakly supervised classification framework. In this task algorithms have to deal with datasets where each instance ...

• Dirichlet process mixture models for non-stationary data streams 

  Casado, I.; Pérez, A. (2022)

  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 candidate for these scenarios. However, reliable ...

• Machine learning from crowds using candidate set-based labelling 

  Beñaran-Muñoz, I.; Hernandez, J.; Pérez, A. (2022)

  Crowdsourcing is a popular cheap alternative in machine learning for gathering information from a set of annotators. Learning from crowd-labelled data involves dealing with its inherent uncertainty and inconsistencies. In ...

• New Knowledge about the Elementary Landscape Decomposition for Solving the Quadratic Assignment Problem 

  Benavides, X.; Ceberio, J.; Hernando, L.; Lozano, J.A. (2023-07-15)

  Previous works have shown that studying the characteristics of the Quadratic Assignment Problem (QAP) is a crucial step in gaining knowledge that can be used to design tailored meta-heuristic algorithms. One way to analyze ...

• Minimax Risk Classifiers with 0-1 Loss 

  Mazuelas, S.; Romero, M.; Grunwald, P. (2023-07-01)

  Supervised classification techniques use training samples to learn a classification rule with small expected 0 -1 loss (error probability). Conventional methods enable tractable learning and provide out-of-sample ...

• Transitions from P to NP-hardness: the case of the Linear Ordering Problem 

  Elorza, A.; Hernando, L.; Lozano, J.A. (2022)

  In this paper we evaluate how constructive heuristics degrade when a problem transits from P to NP-hard. This is done by means of the linear ordering problem. More specifically, for this problem we prove that the objective ...

• 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 ...

• Time-Varying Lyapunov Control Laws with Enhanced Estimation of Distribution Algorithm for Low-Thrust Trajectory Design 

  Shirazi, A.; Holt, H.; Armellin, R.; Baresi, N. (2023-04-30)

  Enhancements in evolutionary optimization techniques are rapidly growing in many aspects of engineering, specifically in astrodynamics and space trajectory optimization and design. In this chapter, the problem of optimal ...

• A Variational Learning Approach for Concurrent Distance Estimation and Environmental Identification 

  Li, Y; Mazuelas, S.; Shen, Y. (2023-02-01)

  Wireless propagated signals encapsulate rich information for high-accuracy localization and environment sensing. However, the full exploitation of positional and environmental features as well as their correlation remains ...

• Learning the progression patterns of treatments using a probabilistic generative model 

  Zaballa, O.; Pérez, A.; Gómez-Inhiesto, E.; Acaiturri-Ayesta, T.; Lozano, J.A. (2022-12-15)

  Modeling a disease or the treatment of a patient has drawn much attention in recent years due to the vast amount of information that Electronic Health Records contain. This paper presents a probabilistic generative model ...

• Trajectory optimization of space vehicle in rendezvous proximity operation with evolutionary feasibility conserving techniques 

  Shirazi, A.; Ceberio, J.; Lozano, J.A. (2022-10-09)

  In this paper, a direct approach is developed for discovering optimal transfer trajectories of close-range rendezvous of satellites considering disturbances in elliptical orbits. The control vector representing the inputs ...

• Ad-Hoc Explanation for Time Series Classification 

  Abanda, A.; Mori, U.; Lozano, J.A. (2022)

  In this work, a perturbation-based model-agnostic explanation method for time series classification is presented. One of the main novelties of the proposed method is that the considered perturbations are interpretable and ...

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