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Now showing items 1-10 of 27

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...