Learning – machine-learning-2020

PhD1

Statistical learning approaches for global optimization Emile Contal Evolutionary modelling of industrial systems with genetic programming Birkan, Can Development of a hybrid genetic programming technique for computationally expensive optimisation problems Umberto Armani Enhancing genetic programming for predictive modeling König, Rikard BEHAVIOURAL SYNTHESIS OF ANALOGUE INTEGRATED CIRCUITS SIMON JAMES PARISH … Read more

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Les modeles generatifs en classification supervisee et applications a la categorisation d’images et a la fiabilite industrielle Guillaume Bouchard Correspondance entre régression par processus Gaussien et splines d’interpolation sous contraintes linéaires de type inégalité. Théorie et applications Hassan Maatouk Contribution à l’amélioration des techniques de la programmation génétique Oussama El Gerari Techniques d’optimisation d eterministe … Read more

BAYESIAN LEARNING FOR NEURAL NETWORKS

Radford M. Neal

Model-based problem solving through symbolic regression via pareto genetic programming

Ekaterina Yurievna Vladislavleva

Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty quanti cation for computer experiments

Francois Bachoc Thesis Presentation

PhD1

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BAYESIAN LEARNING FOR NEURAL NETWORKS

Model-based problem solving through symbolic regression via pareto genetic programming

Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty quanti cation for computer experiments

Efficient Simulation in Financial Risk Management

Covariance Kernels for Fast Automatic Pattern Discovery and Extrapolation with Gaussian Processes

Contribution à l’Amélioration des Techniques de la Programmation Génétique

Enhancing Genetic Programming for Predictive Modeling

BEHAVIOURAL SYNTHESIS OF ANALOGUE INTEGRATED CIRCUITS