• 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 ...

• Learning a Battery of COVID-19 Mortality Prediction Models by Multi-objective Optimization 

  Martinez, M.; García-Gutierrez, S.; Armañanzas, R.; Díaz, A.; Inza, I.; Lozano, J.A. (2022-07-09)

  The COVID-19 pandemic is continuously evolving with drastically changing epidemiological situations which are approached with different decisions: from the reduction of fatalities to even the selection of patients with the ...

• The role of asymmetric prediction losses in smart charging of electric vehicles 

  Straka, M.; Buzna, L.; Refa, N.; Mazuelas, S. (2022-07-01)

  Climate change prompts humanity to look for decarbonisation opportunities, and a viable option is to supply electric vehicles with renewable energy. The stochastic nature of charging demand and renewable generation requires ...

• Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees 

  Alvarez, V.; Mazuelas, S.; Lozano, J.A. (2022-07)

  The statistical characteristics of instance-label pairs often change with time in practical scenarios of supervised classification. Conventional learning techniques adapt to such concept drift accounting for a scalar rate ...

• An active adaptation strategy for streaming time series classification based on elastic similarity measures 

  Oregi, I.; Pérez, A.; Del Ser, J.; Lozano, J.A. (2022-05-21)

  In streaming time series classification problems, the goal is to predict the label associated to the most recently received observations over the stream according to a set of categorized reference patterns. In on-line ...

• Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables 

  li, Y.; Mazuelas, S.; Shen, Y. (2022-05-23)

  Deep generative models (DGMs) and their conditional counterparts provide a powerful ability for general-purpose generative modeling of data distributions. However, it remains challenging for existing methods to address ...

• Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification 

  Li, X.; Mazuelas, S.; Shen, Y. (2021-12-07)

  Received waveforms contain rich information for both range information and environment semantics. However, its full potential is hard to exploit under multipath and non-line- of-sight conditions. This paper proposes a ...

• Implementing the Cumulative Difference Plot in the IOHanalyzer 

  Arza, E.; Ceberio, J.; Irurozki, E.; Pérez, A. (2022-07)

  The IOHanalyzer is a web-based framework that enables an easy visualization and comparison of the quality of stochastic optimization algorithms. IOHanalyzer offers several graphical and statistical tools analyze the results ...

• A Resource Sharing Game for the Freshness of Status Updates 

  Miguelez, F.; Doncel, J.; Ayesta, U. (2021-09-01)

• Rank aggregation for non-stationary data streams 

  Irurozki, E.; Pérez, A.; Lobo, J.L.; Del Ser, J. (2022)

  The problem of learning over non-stationary ranking streams arises naturally, particularly in recommender systems. The rankings represent the preferences of a population, and the non-stationarity means that the distribution ...

• A Deep Learning Approach for Generating Soft Range Information from RF Data 

  li, Y.; Mazuelas, S.; Shen, Y. (2022-01-24)

  Radio frequency (RF)-based techniques are widely adopted for indoor localization despite the challenges in extracting sufficient information from measurements. Soft range information (SRI) offers a promising alternative ...

• Time Series Classifier Recommendation by a Meta-Learning Approach 

  Abanda, A.; Mori, U.; Lozano, J.A. (2022-03-26)

  This work addresses time series classifier recommendation for the first time in the literature by considering several recommendation forms or meta-targets: classifier accuracies, complete ranking, top-M ranking, best set ...

• Analysis of Dominant Classes in Universal Adversarial Perturbations 

  Vadillo, J.; Santana, R.; Lozano, J.A. (2022)

  The reasons why Deep Neural Networks are susceptible to being fooled by adversarial examples remains an open discussion. Indeed, many differ- ent strategies can be employed to efficiently generate adversarial attacks, some ...

• Generalized Maximum Entropy for Supervised Classification 

  Mazuelas, S.; Shen, Y.; Pérez, A. (2022-04)

  The maximum entropy principle advocates to evaluate events’ probabilities using a distribution that maximizes entropy among those that satisfy certain expectations’ constraints. Such principle can be generalized for ...

• K-means for Evolving Data Streams 

  Bidaurrazaga, A.; Pérez, A.; Capó, M. (2021-01-01)

  Nowadays, streaming data analysis has become a relevant area of research in machine learning. Most of the data streams available are unlabeled, and thus it is necessary to develop specific clustering techniques that take ...

• Derivation of a Cost-Sensitive COVID-19 Mortality Risk Indicator Using a Multistart Framework 

  Armañanzas, R.; Diaz, A.; Martinez, M.; Mazuelas, S. (2022-01-14)

  The overall global death rate for COVID-19 patients has escalated to 2.13% after more than a year of worldwide spread. Despite strong research on the infection pathogenesis, the molecular mechanisms involved in a fatal ...

• A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform 

  Li, Y.; Mazuelas, S.; Shen, Y. (2021-12-30)

  Localization systems based on ultra-wide band (UWB) measurements can have unsatisfactory performance in harsh environments due to the presence of non-line-of-sight (NLOS) errors. Learning-based methods for error ...

• Deep GEM-based network for weakly supervised UWB ranging error mitigation 

  Li, Y; Mazuelas, S.; Shen, Y. (2021-12-30)

  Ultra-wideband (UWB)-based techniques, while becoming mainstream approaches for high-accurate positioning, tend to be challenged by ranging bias in harsh environments. The emerging learning-based methods for error ...

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