Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning

The field of machine learning (ML) has experienced a major boom in the past years, both in theoretical developments and application areas. However, the rampant adoption of ML methodologies has revealed that models are usually adopted to make decisions without taking into account the uncertainties in...

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Detalles Bibliográficos
Autor: Gallego Alcalá, Víctor
Tipo de recurso: tesis doctoral
Fecha de publicación:2022
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/3546
Acceso en línea:https://hdl.handle.net/20.500.14352/3546
Access Level:acceso abierto
Palabra clave:004.85(043.2)
Machine Learning
Aprendizaje automático (Inteligencia artificial)
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
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oai_identifier_str oai:docta.ucm.es:20.500.14352/3546
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spelling Contributions to Large Scale Bayesian Inference and Adversarial Machine LearningContribuciones a la Inferencia Bayesiana a Gran Escala y al Aprendizaje Automático AdversarioGallego Alcalá, Víctor004.85(043.2)Machine LearningAprendizaje automático (Inteligencia artificial)Inteligencia artificial (Informática)1203.04 Inteligencia ArtificialThe field of machine learning (ML) has experienced a major boom in the past years, both in theoretical developments and application areas. However, the rampant adoption of ML methodologies has revealed that models are usually adopted to make decisions without taking into account the uncertainties in their predictions. More critically, they can be vulnerable to adversarial examples, strategic manipulations of the data with the goal of fooling those systems. For instance, in retailing, a model may predict very high expected sales for the next week, given a certain advertisement budget. However, the predicted variance may also be quite big, thus making the prediction almost useless depending on the risk tolerance of the company. Similarly, in the case of spam detection, an attacker may insert additional words in a given spam email to evade being classified as spam by making it to appear more legit. Thus, we believe that developing ML systems that take into account predictive uncertainties and are robust against adversarial examples is a must for critical, real-world tasks. This thesis is a step towards achieving this goal...Universidad Complutense de MadridRíos Insua, DavidGómez-Ullate Oteiza, y DavidUniversidad Complutense de Madrid20222022-03-2820222022-03-28doctoral thesishttp://purl.org/coar/resource_type/c_db06info:eu-repo/semantics/doctoralThesisapplication/pdfhttps://hdl.handle.net/20.500.14352/3546reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/35462026-06-02T12:44:21Z
dc.title.none.fl_str_mv Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning
Contribuciones a la Inferencia Bayesiana a Gran Escala y al Aprendizaje Automático Adversario
title Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning
spellingShingle Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning
Gallego Alcalá, Víctor
004.85(043.2)
Machine Learning
Aprendizaje automático (Inteligencia artificial)
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
title_short Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning
title_full Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning
title_fullStr Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning
title_full_unstemmed Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning
title_sort Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning
dc.creator.none.fl_str_mv Gallego Alcalá, Víctor
author Gallego Alcalá, Víctor
author_facet Gallego Alcalá, Víctor
author_role author
dc.contributor.none.fl_str_mv Ríos Insua, David
Gómez-Ullate Oteiza, y David
Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 004.85(043.2)
Machine Learning
Aprendizaje automático (Inteligencia artificial)
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
topic 004.85(043.2)
Machine Learning
Aprendizaje automático (Inteligencia artificial)
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
description The field of machine learning (ML) has experienced a major boom in the past years, both in theoretical developments and application areas. However, the rampant adoption of ML methodologies has revealed that models are usually adopted to make decisions without taking into account the uncertainties in their predictions. More critically, they can be vulnerable to adversarial examples, strategic manipulations of the data with the goal of fooling those systems. For instance, in retailing, a model may predict very high expected sales for the next week, given a certain advertisement budget. However, the predicted variance may also be quite big, thus making the prediction almost useless depending on the risk tolerance of the company. Similarly, in the case of spam detection, an attacker may insert additional words in a given spam email to evade being classified as spam by making it to appear more legit. Thus, we believe that developing ML systems that take into account predictive uncertainties and are robust against adversarial examples is a must for critical, real-world tasks. This thesis is a step towards achieving this goal...
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-03-28
2022
2022-03-28
dc.type.none.fl_str_mv doctoral thesis
http://purl.org/coar/resource_type/c_db06
dc.type.openaire.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/3546
url https://hdl.handle.net/20.500.14352/3546
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad Complutense de Madrid
publisher.none.fl_str_mv Universidad Complutense de Madrid
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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