Learning probability distributions generated by finite-state machines

We 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 and PAC formal models. The methods we review are state merging and state splitting methods for probabilistic deterministic automata and the recently developed spectral method for nondeterministic probabilistic automata. In both cases, we derive them from a high-level algorithm described in terms of the Hankel matrix of the distribution to be learned, given as an oracle, and then describe how to adapt that algorithm to account for the error introduced by a finite sample.Peer ReviewedPostprint (author's final draft

The Consistency dimension and distribution-dependent learning from queries

We 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 membership queries of its subclasses obtained by restricting the instance space. Then we propose and study two models of query learning in which there is a probability distribution on the instance space, both as an application of the tools developed from the combinatorial characterization and as models of independent interest.Postprint (published version

Web apps and imprecise probabilities

We propose a model for the behaviour of Web apps in the unreliable WWW. Web apps are described by orchestrations. An orchestration mimics the personal use of the Web by defining the way in which Web services are invoked. The WWW is unreliable as poorly maintained Web sites are prone to fail. We model this source of unreliability trough a probabilistic approach. We assume that each site has a probability to fail. Another source of uncertainty is the traffic congestion. This can be observed as a non-deterministic behaviour induced by the variability in the response times. We model non-determinism by imprecise probabilities. We develop here an ex-ante normal to characterize the behaviour of finite orchestrations in the unreliable Web. We show the existence of a normal form under such semantics for orchestrations using asymmetric parallelism.Peer ReviewedPostprint (author's final draft

Power to invest

In 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 desired return and the R_i's are the return of the assets (to invest in). We introducePeer ReviewedPostprint (author's final draft

Refining the imprecise meaning of non-determinism in the Web by strategic games

Nowadays 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 an inherent degree of uncertainty due to incomplete Web knowledge and orchestration semantics are characterized in terms of imprecise probabilistic choices. We consider two aspects in this imprecise semantic characterization. First, when local knowledge (even imprecise) of some part of the Web increases, this knowledge goes smoothly through the whole orchestration. We deal formally with this aspect introducing orchestration refinements. Second, we analyze refinement under uncertainty in the case of parallel composition. Uncertain knowledge is modeled by an uncertainty profile. Such profiles allow us to look at the uncertainty through a zero-sum game, called angel/daemon-game. We propose to use the structure of the Nash equilibria to refine uncertainty. In this case the information improves not through cooperation but through the angel and daemon competition.Peer ReviewedPostprint (author's final draft

Bootstrapping and learning PDFA in data streams

Best Student Paper ICGI 2012Markovian models with hidden state are widely-used formalisms for modeling sequential phenomena. Learnability of these models has been well studied when the sample is given in batch mode, and algorithms with PAC-like learning guarantees exist for specic classes of models such as Probabilistic Deterministic Finite Automata (PDFA). Here we focus on PDFA and give an algorithm for infering models in this class under the stringent data stream scenario: unlike existing methods, our algorithm works incrementally and in one pass, uses memory sublinear in the stream length, and processes input items in amortized constant time. We provide rigorous PAC-like bounds for all of the above, as well as an evaluation on synthetic data showing that the algorithm performs well in practice. Our algorithm makes a key usage of several old and new sketching techniques. In particular, we develop a new sketch for implementing bootstrapping in a streaming setting which may be of independent interest. In experiments we have observed that this sketch yields important reductions in the examples required for performing some crucial statistical tests in our algorithm.Peer ReviewedAward-winningPostprint (published version

Programming 1 and sustainability

Computer programming is an essential skill for today's engineers, and sustainability plays a role of growing interest in any of the design phases of an engineering project. The fundamentals of sustainability, with basic concepts such as "carbon intensity", must be covered in any engineering curriculum. We propose a basic computer programming course in which the lab sessions incorporate exercises related to the computation of environmental impacts. For instance, an exercise might request the computation of the carbon footprint of the lab sessions during a whole year. Lab sessions use an automatic evaluation server, so called Jutge.org that assesses about the correctness of the programs submitted by the students. New exercises concerning sustainability topics are included in the Jutge.org course. The course involves the effort of a multidisciplinary team. First, lecturers on basic programming are required. An expert on automatic evaluation of computer programs is essential to prepare the statements and the test sets of the proposed exercises. Finally, the advice of economists and sustainability experts is crucial to guarantee judicious conclusions are drawn from each exercise. Forming a team with this profile is a challenging task. Our Computer Science (CS) department lectures basic programming courses to more than 1700 students/year. The success of this approach could bring a substantial social impact in our ecosystem

Ordocoordinación: cómo organizar 700 estudiantes en un nuevo campus (y no morir en el intento)

Since Autumn Term 2017 the Department of Computer Science of the Universitat Politecnica de Catalunya UPC-BarcelonaTech is in charge of teaching ”Fundamentals of Programming” in the new DiagonalBeso ´s Campus, at EEBE School. This new endeavour had to face two particular challenges: First, due to organizing constraints, it had to be organized at the same time it was being first taught. Second, all the numbers involved were large. In effect, in Autumn Term 2017, 686 students enroled, with a teaching staff of 18 instructors, and 108 laboratory tests being prepared. To deal with these challenges, we agreed to coordinate ourselves in a particular way which we name ordocoordination. We define ordocoordation as a flexible and quick particular way of coordination in which teachers generate and agree on a minimum set of rules. It is a bottom-up procedure, requiring taking quick decisions. As a consequence of applying this particular coordination, the number of sent emails has been a large one: in Autumn Term 2017: 350 ×18 = 6300 emails were interchanged. We believe that this approach deserves to be reported, and also that it is relevant to other subjects.A partir del Q1 de 2017 el Departamento de CS de la UPC se ocupa de la docencia de Informática I en el nuevo campus de la EEBE. Dicha docencia ha sido singular en dos aspectos. Primero, hubo que organizarla al mismo tiempo que se impartía. Segundo, todos los números son grandes. En el Q1 de 2017, hubo 686 estudiantes matriculados, 18 docentes y se prepararon 108 exámenes de laboratorio. Para tratar con estas singularidades hemos adoptado una forma de coordinación a la que hemos llamado ordocoordinación. Es una coordinación flexible y rápida en la que los docentes generan y consensúan un conjunto mínimo de reglas. Es de abajo a arriba y requiere una toma de decisiones ágil, por lo que el número de emails ha sido importante. En Q1 de 2017: 350 x 18 = 6300 emails. Creemos que esta aproximación merece ser explicada y que puede ser aplicada a otras asignaturas.Peer ReviewedPostprint (published version

A note on bounded query learning

We study in this note the relationships between two usual models of learning via queries: the so-called exact and bounded learning models. One of our goals is to point out under which conditions the learning dimension functions, as the abstract identification dimension, originally defined assuming the bounded model can be also used in the exact setting and how the results obtained by using them have to be interpreted

On the query complexity of quantum learners

This paper introduces a framework for quantum exact learning via queries, the so-called quantum protocol. It is shown that usual protocols in the classical learning setting have quantum counterparts. A combinatorial notion, the general halving dimension, is also introduced. Given a quantum protocol and a target concept class, the general halving dimension provides a lower bound on the number of queries that a quantum algorithm needs to learn. For usual protocols, this lower bound is also valid even if only involution oracle teachers are considered. The general halving dimension also approximates the query complexity of ordinary randomized learners. From these bounds we conclude that any quantum polynomially query learnable concept class must be also polynomially learnable in the classical setting