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AUTOMAT[R]IX: learning simple matrix pipelines

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AUTOMAT[R]IX: learning simple matrix pipelines

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dc.contributor.author Contreras-Ochando, Lidia es_ES
dc.contributor.author Ferri Ramírez, César es_ES
dc.contributor.author Hernández-Orallo, José es_ES
dc.date.accessioned 2022-07-08T18:05:04Z
dc.date.available 2022-07-08T18:05:04Z
dc.date.issued 2021-04 es_ES
dc.identifier.issn 0885-6125 es_ES
dc.identifier.uri http://hdl.handle.net/10251/183987
dc.description.abstract [EN] Matrices are a very common way of representing and working with data in data science and artificial intelligence. Writing a small snippet of code to make a simple matrix transformation is frequently frustrating, especially for those people without an extensive programming expertise. We present AUTOMAT[R]IX, a system that is able to induce R program snippets from a single (and possibly partial) matrix transformation example provided by the user. Our learning algorithm is able to induce the correct matrix pipeline snippet by composing primitives from a library. Because of the intractable search space-exponential on the size of the library and the number of primitives to be combined in the snippet, we speed up the process with (1) a typed system that excludes all combinations of primitives with inconsistent mapping between input and output matrix dimensions, and (2) a probabilistic model to estimate the probability of each sequence of primitives from their frequency of use and a text hint provided by the user. We validate AUTOMAT[R]IX with a set of real programming queries involving matrices from Stack Overflow, showing that we can learn the transformations efficiently, from just one partial example es_ES
dc.description.sponsorship We thank the anonymous reviewers for their comments, which have improved the paper significantly. This research was supported by the EU (FEDER) and the Spanish MINECO RTI2018-094403B-C32 and the Generalitat Valenciana PROMETEO/2019/098. L. Contreras-Ochando was also supported by the Spanish MECD Grant (FPU15/03219). J. Hernandez-Orallo is also funded by FLI (RFP2-152). es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Machine Learning es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Automating data science es_ES
dc.subject Inductive programming es_ES
dc.subject Program synthesis es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.title AUTOMAT[R]IX: learning simple matrix pipelines es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s10994-021-05950-7 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MECD//FPU15%2F03219/ES/FPU15%2F03219/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/FLI//RFP2-152/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//PROMETEO%2F2019%2F098//DEEPTRUST/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//RTI2018-094403-B-C32-AR//RAZONAMIENTO FORMAL PARA TECNOLOGIAS FACILITADORAS Y EMERGENTES/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Contreras-Ochando, L.; Ferri Ramírez, C.; Hernández-Orallo, J. (2021). AUTOMAT[R]IX: learning simple matrix pipelines. Machine Learning. 110(4):779-799. https://doi.org/10.1007/s10994-021-05950-7 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s10994-021-05950-7 es_ES
dc.description.upvformatpinicio 779 es_ES
dc.description.upvformatpfin 799 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 110 es_ES
dc.description.issue 4 es_ES
dc.relation.pasarela S\441745 es_ES
dc.contributor.funder GENERALITAT VALENCIANA es_ES
dc.contributor.funder Future of Life Institute es_ES
dc.contributor.funder MINISTERIO DE EDUCACION es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
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upv.costeAPC 2670 es_ES


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