A computable theory for learning Bayesian networks based on MAP-MDL principles

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Minimum Description Length Revisited

This is an up-to-date introduction to and overview of the Minimum Description Length (MDL) Principle, a theory of inductive inference that can be applied to general problems in statistics, machine learning and pattern recognition. While MDL was originally based on data compression ideas, this introduction can be read without any knowledge thereof. It takes into account all major developments since 2007, the last time an extensive overview was written. These include new methods for model selection and averaging and hypothesis testing, as well as the first completely general definition of {\em MDL estimators}. Incorporating these developments, MDL can be seen as a powerful extension of both penalized likelihood and Bayesian approaches, in which penalization functions and prior distributions are replaced by more general luckiness functions, average-case methodology is replaced by a more robust worst-case approach, and in which methods classically viewed as highly distinct, such as AIC vs BIC and cross-validation vs Bayes can, to a large extent, be viewed from a unified perspective

Building Bayesian Networks: Elicitation, Evaluation, and Learning

As a compact graphical framework for representation of multivariate probabilitydistributions, Bayesian networks are widely used for efficient reasoning underuncertainty in a variety of applications, from medical diagnosis to computertroubleshooting and airplane fault isolation. However, construction of Bayesiannetworks is often considered the main difficulty when applying this frameworkto real-world problems. In real world domains, Bayesian networks are often built by knowledge engineering approach. Unfortunately, eliciting knowledge from domain experts isa very time-consuming process, and could result in poor-quality graphicalmodels when not performed carefully. Over the last decade, the research focusis shifting more towards learning Bayesian networks from data, especially withincreasing volumes of data available in various applications, such asbiomedical, internet, and e-business, among others.Aiming at solving the bottle-neck problem of building Bayesian network models, thisresearch work focuses on elicitation, evaluation and learning Bayesiannetworks. Specifically, the contribution of this dissertation involves the research in the following five areas:a) graphical user interface tools forefficient elicitation and navigation of probability distributions, b) systematic and objective evaluation of elicitation schemes for probabilistic models, c)valid evaluation of performance robustness, i.e., sensitivity, of Bayesian networks,d) the sensitivity inequivalent characteristic of Markov equivalent networks, and the appropriateness of using sensitivity for model selection in learning Bayesian networks,e) selective refinement for learning probability parameters of Bayesian networks from limited data with availability of expert knowledge. In addition, an efficient algorithm for fast sensitivity analysis is developed based on relevance reasoning technique. The implemented algorithm runs very fast and makes d) and e) more affordable for real domain practice

Représentation et apprentissage de préférences

La modélisation des préférences par le biais de formalismes de représentation compacte fait l'objet de travaux soutenus en intelligence artificielle depuis plus d'une quinzaine d'années. Ces formalismes permettent l'expression de modèles suffisamment flexibles et riches pour décrire des comportements de décision complexes. Pour être intéressants en pratique, ces formalismes doivent de plus permettre l'élicitation des préférences de l'utilisateur, et ce en restant à un niveau admissible d'interaction. La configuration de produits combinatoires dans sa version business to customer et la recherche à base de préférences constituent de bons exemples de ce type de problème de décision où les préférences de l'utilisateur ne sont pas connues a priori. Dans un premier temps, nous nous sommes penchés sur l'apprentissage de GAI-décompositions. Nous verrons qu'il est possible d'apprendre une telle représentation en temps polynomial en passant par un système d'inéquations linéaires. Dans un second temps, nous proposerons une version probabiliste des CP-nets permettant la représentation de préférences multi-utilisateurs afin de réduire le temps nécessaire à l'apprentissage des préférences d'un utilisateur. Nous étudierons les différentes requêtes que l'on peut utiliser avec une telle représentation, puis nous nous pencherons sur la complexité de ces requêtes. Enfin, nous verrons comment apprendre ce nouveau formalisme, soit grâce à un apprentissage hors ligne à partir d'un ensemble d'objets optimaux, soit grâce à un apprentissage en ligne à partir d'un ensemble de questions posées à l'utilisateur

Probabilistic modeling and reasoning in multiagent decision systems

Ph.DDOCTOR OF PHILOSOPH

Intelligence in 5G networks

Over the past decade, Artificial Intelligence (AI) has become an important part of our daily lives; however, its application to communication networks has been partial and unsystematic, with uncoordinated efforts that often conflict with each other. Providing a framework to integrate the existing studies and to actually build an intelligent network is a top research priority. In fact, one of the objectives of 5G is to manage all communications under a single overarching paradigm, and the staggering complexity of this task is beyond the scope of human-designed algorithms and control systems. This thesis presents an overview of all the necessary components to integrate intelligence in this complex environment, with a user-centric perspective: network optimization should always have the end goal of improving the experience of the user. Each step is described with the aid of one or more case studies, involving various network functions and elements. Starting from perception and prediction of the surrounding environment, the first core requirements of an intelligent system, this work gradually builds its way up to showing examples of fully autonomous network agents which learn from experience without any human intervention or pre-defined behavior, discussing the possible application of each aspect of intelligence in future networks