Optimal transport for machine learning: theory and applications
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Institucional do FGV (FGV Repositório Digital) |
Texto Completo: | https://hdl.handle.net/10438/30407 |
Resumo: | O que os operadores de produção de petróleo valorizam ao comprar produtos químicos?: uma análise sobre a percepção de valor na decisão de compra ou contratação de um provedor de especialidades químicas no mercado de óleo e gásIn recent years, advances in Optimal Transport have led to a surge of applications in fields such as Economics, Quantitative Finance and Signal Processing, among others. One area in which it has been found particularly successful is Machine Learning. The development of computationally efficient methods for solving Optimal Transport problems opened doors for creating Machine Learning algorithms using concepts from Optimal Transport. These new algorithms encompass many different sub-areas such as Transfer Learning, Clustering, Dimensionality Reduction, Generative Models, just to name some. This work provides an overview of the different ways in which Optimal Transport has been used in Machine Learning, thus helping Machine Learning researchers to better understand its impact in the field and how to use it. This thesis first introduces the main theoretical and computational aspects of Optimal Transport theory in an accessible way to Machine Learning researchers, followed by a semi-systematic literature review focusing on the main uses of Optimal Transport in Machine Learning. |
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Barreira, Davi SalesEscolas::EMApOliveira, Roberto ImbuzeiroPaccanaro, AlbertoMendes, Eduardo Fonseca2021-04-26T13:39:07Z2021-04-26T13:39:07Z2021-03-25https://hdl.handle.net/10438/30407O que os operadores de produção de petróleo valorizam ao comprar produtos químicos?: uma análise sobre a percepção de valor na decisão de compra ou contratação de um provedor de especialidades químicas no mercado de óleo e gásIn recent years, advances in Optimal Transport have led to a surge of applications in fields such as Economics, Quantitative Finance and Signal Processing, among others. One area in which it has been found particularly successful is Machine Learning. The development of computationally efficient methods for solving Optimal Transport problems opened doors for creating Machine Learning algorithms using concepts from Optimal Transport. These new algorithms encompass many different sub-areas such as Transfer Learning, Clustering, Dimensionality Reduction, Generative Models, just to name some. This work provides an overview of the different ways in which Optimal Transport has been used in Machine Learning, thus helping Machine Learning researchers to better understand its impact in the field and how to use it. This thesis first introduces the main theoretical and computational aspects of Optimal Transport theory in an accessible way to Machine Learning researchers, followed by a semi-systematic literature review focusing on the main uses of Optimal Transport in Machine Learning.engOptimal transportWasserstein distanceMachine learningLiterature reviewDistância de WassersteinMatemáticaProblemas de transporte (Programação)Aprendizado do computadorOtimização matemáticaAnálise combinatóriaOptimal transport for machine learning: theory and applicationsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis2021-03-25reponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVinfo:eu-repo/semantics/openAccessLICENSElicense.txtlicense.txttext/plain; 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|
dc.title.eng.fl_str_mv |
Optimal transport for machine learning: theory and applications |
title |
Optimal transport for machine learning: theory and applications |
spellingShingle |
Optimal transport for machine learning: theory and applications Barreira, Davi Sales Optimal transport Wasserstein distance Machine learning Literature review Distância de Wasserstein Matemática Problemas de transporte (Programação) Aprendizado do computador Otimização matemática Análise combinatória |
title_short |
Optimal transport for machine learning: theory and applications |
title_full |
Optimal transport for machine learning: theory and applications |
title_fullStr |
Optimal transport for machine learning: theory and applications |
title_full_unstemmed |
Optimal transport for machine learning: theory and applications |
title_sort |
Optimal transport for machine learning: theory and applications |
author |
Barreira, Davi Sales |
author_facet |
Barreira, Davi Sales |
author_role |
author |
dc.contributor.unidadefgv.por.fl_str_mv |
Escolas::EMAp |
dc.contributor.member.none.fl_str_mv |
Oliveira, Roberto Imbuzeiro Paccanaro, Alberto |
dc.contributor.author.fl_str_mv |
Barreira, Davi Sales |
dc.contributor.advisor1.fl_str_mv |
Mendes, Eduardo Fonseca |
contributor_str_mv |
Mendes, Eduardo Fonseca |
dc.subject.eng.fl_str_mv |
Optimal transport Wasserstein distance Machine learning Literature review |
topic |
Optimal transport Wasserstein distance Machine learning Literature review Distância de Wasserstein Matemática Problemas de transporte (Programação) Aprendizado do computador Otimização matemática Análise combinatória |
dc.subject.por.fl_str_mv |
Distância de Wasserstein |
dc.subject.area.por.fl_str_mv |
Matemática |
dc.subject.bibliodata.por.fl_str_mv |
Problemas de transporte (Programação) Aprendizado do computador Otimização matemática Análise combinatória |
description |
O que os operadores de produção de petróleo valorizam ao comprar produtos químicos?: uma análise sobre a percepção de valor na decisão de compra ou contratação de um provedor de especialidades químicas no mercado de óleo e gásIn recent years, advances in Optimal Transport have led to a surge of applications in fields such as Economics, Quantitative Finance and Signal Processing, among others. One area in which it has been found particularly successful is Machine Learning. The development of computationally efficient methods for solving Optimal Transport problems opened doors for creating Machine Learning algorithms using concepts from Optimal Transport. These new algorithms encompass many different sub-areas such as Transfer Learning, Clustering, Dimensionality Reduction, Generative Models, just to name some. This work provides an overview of the different ways in which Optimal Transport has been used in Machine Learning, thus helping Machine Learning researchers to better understand its impact in the field and how to use it. This thesis first introduces the main theoretical and computational aspects of Optimal Transport theory in an accessible way to Machine Learning researchers, followed by a semi-systematic literature review focusing on the main uses of Optimal Transport in Machine Learning. |
publishDate |
2021 |
dc.date.accessioned.fl_str_mv |
2021-04-26T13:39:07Z |
dc.date.available.fl_str_mv |
2021-04-26T13:39:07Z |
dc.date.issued.fl_str_mv |
2021-03-25 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10438/30407 |
url |
https://hdl.handle.net/10438/30407 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
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