ML

Machine Learning

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Our goal is to develop novel and efficient machine learning algorithms able to deal with new data-related practical problems. We also pursue the mathematical modeling of these algorithms in order to provide theoretical guarantees of their performance.

In the machine learning research line we deal with data problems coming from different scenarios: industry, biosciences, health, economy, etc. We pursue the developments of new machine learning algorithms that can efficiently tackle these problems. Particularly, we consider problems that account for a variety of data types: from time series, to steaming data or images and speech, and a wide range of modelization techniques and mathematical formalisms such as: probabilistic graphical models, Bayesian approaches, deep learning, etc.

The research carried out in the machine learning line is inspired in problems that appear in other scientific, technological or economical disciplines. We develop new machine learning methods and algorithms related with the main data analysis activities such as clustering, supervised classification, feature subset selection, etc. to solve this kind of problems. Based on the specific characteristics of the problem at hand, we design tailored but general algorithms that extract as much information as possible from the available data providing efficient machine learning models that solve the problem.

In addition to that, we also develop mathematical tools able to model the behavior and performance of the algorithms: studying their convergence, the estimation of the performance, the behavior of the algorithms in terms of computational time and memory requirements, etc.

During the last years the machine learning line has worked with different machine learning problems and algorithms. Particularly, we can emphasize the work done in the area of time series mining and data streaming, the adaptation of classical clustering algorithms such as k-means or k-medoids to massive data environments, the probabilistic modeling of permutations and ranked data or the developments in anomaly detection, and the analysis of crowd learning environments.

In terms of formalisms, we strongly rely on probabilistic modeling, using different tools and techniques such as probabilistic graphical models and Gaussian process to name, which in most cases are learned under a Bayesian perspective. We also pursue the use of deep learning when we consider it the most appropriate technique for the problem at hand.

Espazio ontzien transferentzia ibilbideetarako sekuentzia hierarkikoen optimizazioa meta-heuristikaren erabileran oinarritua

Azalpena: Bideo honek meta-heuristikaren erabileran oinarritutako espazio ontzien transferentzia ibilbideetarako sekuentzia hierarkikoen optimizazioa agertzen du. Hiru algoritmo ebolutibo mota erabili dira (algoritmo genetikoa, partikula erlekume bidezko optimizazioa eta banaketa algoritmoen estimazioa barne) orientazio planteamendu optimorako, bultzada txikiko ibilbideak baliatuta. Algoritmo bakoitzarentzat hasierako orbita desberdinak hartzen dira kontuan, baina orbitek forma eta orientazio bera izatea espero da misio espaziala amaitzean.

Author Abolfazl Shirazi

Astelehena, 06 Eka 2022

Espazioko irispide luzeko bulkada anitzeko hitzordua Lambert planteamendu diskretizatu ebolutiboaren bitartez

Sateliteak espazioko orbiten artean transferitzea lan latza da. Sistema espazialen konplexutasuna dela eta, transferentzia ibilbide optimoak aurkitzeko planteamendu efizienteak behar dira. Bideo honetan satelitearen mugimenduaren simulazioa ikus daiteke misio espazial batean. Orbitaren transferentziarako algoritmo ebolutibo berri bat erabili da. Sateliteak hainbat transferentzia gauzatzen ditu tarteko orbiten artean helmugara iritsi arte. Mekanismo heuristiko berritzaileak erabili dira espazio ontzien ibilbidea diseinatzeko optimizazio algoritmoa garatzeko. Simulazioaren emaitzek algoritmoaren eraginkortasuna adierazten dute sateliteen transferentzia ibilbide optimoak aurkitzeko.

Author Abolfazl Shirazi

2023-11-30 ML

Name: Arza González, Etor

Thesis advisor(s): Pérez, Aritz (BCAM) and Irurozki, Ekhiñe (University of the Basque Country - UPV/EHU)

University: University of the Basque Country (UPV/EHU)

2023-10-27 ML

Contributions to the mathematical modeling of estimation of distribution algorithms and pseudo-boolean functions

Name: Unanue Gual, Imanol

Thesis advisor(s): Merino, María (UPV/EHU) and Lozano, Jose A. (BCAM)

University: University of the Basque Country (UPV/EHU)

2023-11-30 ML

Understanding non-convex optimization problems and stochastic optimization algorithms

Name: Arza González, Etor

Thesis advisor(s): Pérez, Aritz (BCAM) and Irurozki, Ekhiñe (University of the Basque Country - UPV/EHU)

University: University of the Basque Country (UPV/EHU)

2021-03-11 ML

Efficient Meta-Heuristics for Spacecraft Trajectory Optimization

Name: Shirazi, Abolfazl

Thesis advisor(s): Lozano, Jose A.

University: Euskal Herriko Unibertsitatea (UPV/EHU)

2021-02-25 ML

Algorithms for large orienteering problems

Name: Kobeaga, Gorka

Thesis advisor(s): Lozano, Jose A.

University: Euskal Herriko Unibertsitatea (UPV/EHU)

2020-07-23 ML

Advances on time series analysis using elastic measures of similarity

Name: Oregui, Izaskun

Thesis advisor(s): Perez, Aritz

University: Euskal Herriko Unibertsitatea (UPV/EHU)

2019-04-30 ML

K-means for massive data

Name: Capó, Marco

Thesis advisor(s): Lozano, Jose A. eta Pérez, Aritz

University: Euskal Herriko Unibertsitatea (UPV/EHU)

2018-11-30 ML

Theoretical and Methodological Advances in semi-supervised learning and the class-imbalance problem

Name: Ortigosa Hernández, Jonathan

Thesis advisor(s): Lozano, Jose A.

University: Euskal Herriko Unibertsitatea (UPV/EHU)