Browsing by Author "Castro Rabal, Jorge"
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A lower bound for learning distributions generated by probabilistic automata
Balle Pigem, Borja de; Castro Rabal, Jorge; Gavaldà Mestre, Ricard (Springer, 2010)
Conference report
Open AccessKnown algorithms for learning PDFA can only be shown to run in time polynomial in the socalled distinguishability μ of the target machine, besides the number of states and the usual accuracy and confidence parameters. ... 
A New abstract combinatorial dimension for exact learning via queries
Balcázar Navarro, José Luis; Castro Rabal, Jorge; Guijarro Guillem, David (200002)
External research report
Open AccessWe introduce an abstract model of exact learning via queries that can be instantiated to all the query learning models currently in use, while being closer to them than previous unificatory attempts. We present a ... 
A note on bounded query learning
Castro Rabal, Jorge (200504)
External research report
Open AccessWe study in this note the relationships between two usual models of learning via queries: the socalled exact and bounded learning models. One of our goals is to point out under which conditions the learning dimension ... 
A Note on learning decision lists
Castro Rabal, Jorge (199501)
External research report
Open AccessWe show an algorithm that learns decision lists via equivalence queries, provided that a set G including all terms of the target list is given. The algorithm runs in time polynomial in the cardinality of G. From this last ... 
Adaptively learning probabilistic deterministic automata from data streams
Balle Pigem, Borja de; Castro Rabal, Jorge; Gavaldà Mestre, Ricard (201407)
Article
Restricted access  publisher's policyMarkovian models with hidden state are widelyused formalisms for modeling sequential phenomena. Learnability of these models has been well studied when the sample is given in batch mode, and algorithms with PAClike ... 
Bootstrapping and learning PDFA in data streams
Balle Pigem, Borja de; Castro Rabal, Jorge; Gavaldà Mestre, Ricard (2012)
Conference report
Open AccessMarkovian models with hidden state are widelyused formalisms for modeling sequential phenomena. Learnability of these models has been well studied when the sample is given in batch mode, and algorithms with PAClike ... 
Characterizations of some complexity classes between [theta sub 2 super p] and [delta sub 2 super p]
Castro Rabal, Jorge; Seara Ojea, Carlos (1990)
External research report
Open AccessWe give some characterizations of the classes P super NP [0(log super k n)]. First, we show that these classes are equal to classes AC super k1 (N P). Second, we prove that they are also equivalent to some classes defined ... 
Learning nearly monotone kterm DNF
Castro Rabal, Jorge; Guijarro Guillem, David; Lavín Puente, Víctor Angel (199609)
External research report
Open AccessThis note studies the learnability of the class kterm DNF with a bounded number of negations per term. We study the case of learning with membership queries alone, and give tight upper and lower bounds on the ... 
Learning PDFA with asynchronous transitions
Balle Pigem, Borja de; Castro Rabal, Jorge; Gavaldà Mestre, Ricard (Springer, 2010)
Conference lecture
Open AccessIn this paper we extend the PAC learning algorithm due to Clark and Thollard for learning distributions generated by PDFA to automata whose transitions may take varying time lengths, governed by exponential distributions. 
Learning probabilistic automata : a study in state distinguishability
Balle Pigem, Borja de; Castro Rabal, Jorge; Gavaldà Mestre, Ricard (20130218)
Article
Restricted access  publisher's policyKnown algorithms for learning PDFA can only be shown to run in time polynomial in the socalled distinguishability μ of the target machine, besides the number of states and the usual accuracy and confidence parameters. We ... 
Learning probability distributions generated by finitestate machines
Castro Rabal, Jorge; Gavaldà Mestre, Ricard (Springer, 2016)
Part of book or chapter of book
Open AccessWe review methods for inference of probability distributions generated by probabilistic automata and related models for sequence generation. We focus on methods that can be proved to learn in the inference in the limit ... 
Measuring investment opportunities under uncertainty
Castro Rabal, Jorge; Gabarró Vallès, Joaquim; Serna Iglesias, María José (Springer, 2019)
Conference report
Open AccessIn order to make sound economic decisions it is important to measure the possibilities offered by a market in relation to investments. Provided an investment scheme S = (r; R1, . . . , Rn), where r is a lower bound on the ... 
On the query complexity of quantum learners
Castro Rabal, Jorge (200506)
External research report
Open AccessThis paper introduces a framework for quantum exact learning via queries, the socalled quantum protocol. It is shown that usual protocols in the classical learning setting have quantum counterparts. A combinatorial notion, ... 
Ordocoordinación: cómo organizar 700 estudiantes en un nuevo campus (y no morir en el intento)
Castro Rabal, Jorge; Farreres de la Morena, Xavier; Gabarró Vallès, Joaquim; Nivela Alós, M. Pilar Brígida; Pérez Poch, Antoni; Pino Blanco, Elvira; Rivero Almeida, José Miguel (Asociación de Enseñantes Universitarios de la Informática (AENUI), 2018)
Conference report
Open AccessSince Autumn Term 2017 the Department of Computer Science of the Universitat Politecnica de Catalunya UPCBarcelonaTech is in charge of teaching ”Fundamentals of Programming” in the new DiagonalBeso ´s Campus, at EEBE ... 
Power to invest
Castro Rabal, Jorge; Gabarró Vallès, Joaquim; Serna Iglesias, María José (2018)
Conference report
Open AccessIn this post recession time it is important to measure the possibilities offered by a society in relation to investments. To do that, we consider an investment schema I= (R;R_1,...,R_n) where R is a lower bound on the ... 
Query, PACS and simplePAC learning
Castro Rabal, Jorge; Guijarro Guillem, David (199801)
External research report
Open AccessWe study a distribution dependent form of PAC learning that uses probability distributions related to Kolmogorov complexity. We relate the PACS model, defined by Denis, D'Halluin and Gilleron, with the standard simplePAC ... 
Refining the imprecise meaning of nondeterminism in the Web by strategic games
Castro Rabal, Jorge; Gabarró Vallès, Joaquim; Serna Iglesias, María José (Springer, 2019)
Conference report
Open AccessNowadays interactions with the World Wide Web are ubiquitous. Users interact through a number of steps consisting of site calls and handling results that can be automatized as orchestrations. Orchestration results have ... 
Refining indeterministic choice: Imprecise probabilities and strategic thinking
Castro Rabal, Jorge; Gabarró Vallès, Joaquim; Serna Iglesias, María José (202011)
Article
Open AccessOften, uncertainty is present in processes that are part of our routines. Having tools to understand the consequences of unpredictability is convenient. We introduce a general framework to deal with uncertainty in the realm ... 
Simple PAC learning of simple decision lists
Castro Rabal, Jorge; Balcázar Navarro, José Luis (199506)
External research report
Open AccessWe prove that log ndecision lists  the class of decision lists such that all their terms have low Kolmogorov complexity  are learnable in the simple PAC learning model. The proof is based on a transformation from an ... 
The Consistency dimension and distributiondependent learning from queries
Balcázar Navarro, José Luis; Castro Rabal, Jorge; Guijarro Guillem, David (20000501)
External research report
Open AccessWe prove a new combinatorial characterization of polynomial learnability from equivalence queries, and state some of its consequences relating the learnability of a class with the learnability via equivalence and ...