Planning in entropy-regularized Markov decision processes and games

International audienceWe propose SmoothCruiser, a new planning algorithm for estimating the value function in entropy-regularized Markov decision processes and two-player games, given a generative model of the environment. SmoothCruiser makes use of the smoothness of the Bellman operator promoted by the regularization to achieve problem-independent sample complexity of order O(1/ε 4) for a desired accuracy ε, whereas for non-regularized settings there are no known algorithms with guaranteed polynomial sample complexity in the worst case

Surface Electromagnetic Waves Thermally Excited: Radiative Heat Transfer, Coherence Properties and Casimir Forces Revisited in the Near Field

We review in this article the influence of surface waves on the thermally excited electromagnetic field. We study in particular the field emitted at subwalength distances of material surfaces. After reviewing the main properties of surface waves, we introduce the fluctuation-dissipation theorem that allows to model the fluctuating electromagnetic fields. We then analyse the contribution of these waves in a variety of phenomena. They give a leading contribution to the density of electromagnetic states, they produce both temporal coherence and spatial coherence in the near field of planar thermal sources. They can be used to modify radiative properties of surfaces and to design partially spatially coherent sources. Finally, we discuss the role of surface waves in the radiative heat transfer and the theory of dispersion forces at the subwavelength scale.Comment: Redig\'{e} \`{a} la fin de l'ann\'{e}e 2004. Accept\'{e} dans Surface Science Report

Machine Learning for Unsupervised Fraud Detection

Fraud is a threat that most online service providers must address in the development of their systems to ensure an efficient security policy and the integrity of their revenue. Amadeus, a Global Distribution System providing a transaction platform for flight booking by travel agents, is targeted by fraud attempts that could lead to revenue losses and indemnifications. The objective of this thesis is to detect fraud attempts by applying machine learning algorithms to bookings represented by Passenger Name Record history. Due to the lack of labelled data, the current study presents a benchmark of unsupervised algorithms and aggregation methods. It also describes anomaly detection techniques which can be applied to self-organizing maps and hierarchical clustering. Considering the important amount of transactions per second processed by Amadeus back-ends, we eventually highlight potential bottlenecks and alternatives

Probabilistic modeling for novelty detection with applications to fraud identification

Novelty detection is the unsupervised problem of identifying anomalies in test data which significantly differ from the training set. Novelty detection is one of the classic challenges in Machine Learning and a core component of several research areas such as fraud detection, intrusion detection, medical diagnosis, data cleaning, and fault prevention. While numerous algorithms were designed to address this problem, most methods are only suitable to model continuous numerical data. Tackling datasets composed of mixed-type features, such as numerical and categorical data, or temporal datasets describing discrete event sequences is a challenging task. In addition to the supported data types, the key criteria for efficient novelty detection methods are the ability to accurately dissociate novelties from nominal samples, the interpretability, the scalability and the robustness to anomalies located in the training data. In this thesis, we investigate novel ways to tackle these issues. In particular, we propose (i) an experimental comparison of novelty detection methods for mixed-type data (ii) an experimental comparison of novelty detection methods for sequence data, (iii) a probabilistic nonparametric novelty detection method for mixed-type data based on Dirichlet process mixtures and exponential-family distributions and (iv) an autoencoder-based novelty detection model with encoder/decoder modelled as deep Gaussian processes.Comment: PhD thesis; 167 pages, 40 figures, 16 table