LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge
LARCA és un grup de recerca de nivell internacional en temes d'aprenentatge i mineria de dades, anàlisi de dades i lingüística matemàtica. Típicament, abordem els problemes des de principis matemàtics sòlids, usant eines de modelització i tècniques de l'algorísmia, complexitat computacional, teoria d'autòmats, lògica, matemàtica discreta, estadística, i sistemes dinàmics. Estem iniciant
col·laboracions amb empreses i altres institucions per tal d'aplicar les nostres solucions al problemes del món real.
LARCA es un grupo de investigación de nivel internacional en temas de aprendizaje y minería de datos, análisis de datos y lingüística matemática. Típicamente, abordamos los problemas desde principios matemáticos sólidos, usando técnicas de modelización de la algoritmia, la complejidad computacional, teoría de autómatas, lógica, matemática discreta, estadística y sistemas dinámicos. Estamos iniciando colaboraciones con empresas e instituciones para la aplicación de nuestras soluciones a contextos reales.
LARCA is an international research group working on data mining, machine learning, data analysis, and mathematical linguistics. We typically approach problems from sound mathematical principles, using modelling tools and techniques from algorithmics, computational complexity, automata theory, logic, discrete mathematics, statistics, and dynamic systems. We are also starting partnerships with companies and other institutions to apply our solutions to real-world problems.
LARCA is an international research group working on data mining, machine learning, data analysis, and mathematical linguistics. We typically approach problems from sound mathematical principles, using modelling tools and techniques from algorithmics, computational complexity, automata theory, logic, discrete mathematics, statistics, and dynamic systems. We are also starting partnerships with companies and other institutions to apply our solutions to real-world problems.
Col·leccions
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Articles de revista [76]
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Capítols de llibre [10]
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Reports de recerca [36]
Enviaments recents
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Logspace self-reducibility
(Institute of Electrical and Electronics Engineers (IEEE), 1988)
Text en actes de congrés
Accés obertA definition of self-reducibility is proposed to deal with logarithmic space complexity classes. A general property derived from the definition is used to prove known results comparing uniform and nonuniform complexity ... -
Weak-type boundedness of the Fourier transform on rearrangement invariant function spacest
(2018-06-08)
Article
Accés obertWe study several questions about the weak-type boundedness of the Fourier transform F on rearrangement invariant spaces. In particular, we characterize the action of F as a bounded operator from the minimal Lorentz space ... -
Optimization models of natural communication
(2018-07-03)
Article
Accés restringit per política de l'editorialA family of information theoretic models of communication was introduced more than a decade ago to explain the origins of Zipf’s law for word frequencies. The family is a based on a combination of two information theoretic ... -
Spurious ambiguity and focalization
(2018-06)
Article
Accés obertSpurious ambiguity is the phenomenon whereby distinct derivations in grammar may assign the same structural reading, resulting in redundancy in the parse search space and inefficiency in parsing. Understanding the problem ... -
Characterizing approximate-matching dependencies in formal concept analysis with pattern structures
(2018-04-26)
Article
Accés obertFunctional dependencies (FDs) provide valuable knowledge on the relations between attributes of a data table. A functional dependency holds when the values of an attribute can be determined by another. It has been shown ... -
A dependency look at the reality of constituency
(RAM-Verlag, 2018)
Article
Accés obertA comment on “Neurophysiological dynamics of phrase-structure building during sentence processing” by Nelson et al (2017), Proceedings of the National Academy of Sciences USA 114(18), E3669-E3678. -
Global optimality in k-means clustering
(2018-05-01)
Article
Accés restringit per política de l'editorialWe study the problem of finding an optimum clustering, a problem known to be NP-hard. Existing literature contains algorithms running in time proportional to the number of points raised to a power that depends on the ... -
Count-invariance including exponentials
(Association for Computational Linguistics, 2017)
Comunicació de congrés
Accés restringit per política de l'editorialWe define infinitary count-invariance for categorial logic, extending countinvariance for multiplicatives (van Benthem, 1991) and additives and bracket modalities (Valentín et al., 2013) to include exponentials. This ... -
Identifiability and transportability in dynamic causal networks
(Springer, 2017-03)
Article
Accés obertIn this paper, we propose a causal analog to the purely observational dynamic Bayesian networks, which we call dynamic causal networks. We provide a sound and complete algorithm for the identification of causal effects in ... -
Clustering patients with tensor decomposition
(2017)
Comunicació de congrés
Accés obertIn this paper we present a method for the unsupervised clustering of high-dimensional binary data, with a special focus on electronic healthcare records. We present a robust and efficient heuristic to face this problem ... -
Thoughts about disordered thinking: measuring and quantifying the laws of order and disorder
(2017-05-01)
Article
Accés obert -
Classifier selection with permutation tests
(IOS Press, 2017)
Text en actes de congrés
Accés obertThis work presents a content-based recommender system for machine learning classifier algorithms. Given a new data set, a recommendation of what classifier is likely to perform best is made based on classifier performance ...
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