Seminario Aprendizaje de Máquinas

Seminario Conjunto: Aprendizaje de Máquinas y Modelamiento Estocástico. Mini Workshop: Computational Optimal Transport

Event Date: Nov 30, 1999 in Seminario Aprendizaje de Máquinas, Seminars, Stochastic Modeling

Lugar: Sala (B04, piso -1, Beauchef 851) Fecha: Lunes 26, noviembre, 2018. Hora: 1500 – 1700hrs Presentadora: Dr. Elsa Cazelles Título: Statistical properties of barycenters in the Wasserstein space Hora: 1500hrs Abstract: In this work, we discuss the analysis of data in the form of probability measures on R^d. The aim is to provide a better understanding of the usual statistical tools on this space endowed with the Wasserstein distance. The first order statistical analysis is a natural notion to consider, consisting of the study of the Fréchet mean (or barycenter). In particular, we...

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“Privacy Preserving Machine Learning”

Event Date: Nov 30, 1999 in Seminario Aprendizaje de Máquinas, Seminars

Abstract: Los diversos avances en cloud computing y aprendizaje automático han permitido difundir el uso de modelos cliente-servidor, donde un cliente proporciona sus datos y un servidor tiene un modelo de aprendizaje automático para hacer alguna inferencia sobre los datos del cliente. Sin embargo, dado distintos aspectos de la privacidad de los datos, el cliente puede no querer revelar la información requerida por el servidor o incluso dejar que el servidor conozca el resultado del modelo. Esta es una situación común cuando el tipo de información involucrada corresponde a datos médicos,...

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Optimal transport and applications to Data Science

Event Date: Jan 17, 2018 in Seminario Aprendizaje de Máquinas, Seminars

Abstract: Optimal transport (OT) provides rich representations of the discrepancy between probability measures supported on geometric spaces. Recently, thanks to the development of computational techniques, OT has been used to address problems involving massive datasets, as an alternative to usual KL-divergence based approaches. In this talk I will introduce the OT problem and comment on its elementary duality properties. Then, I will present the entropy regularized problem and its (fast) solution via Sinkhorn iterations. Finally, I will overview two applications to Data Science: first,...

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Physics-based Models for Uncertainty Quantification in Chemical Kinetics

Event Date: Jan 15, 2018 in Seminario Aprendizaje de Máquinas, Seminars

Abstract:   Prediction is a core element of science and engineering. Sophisticated mathematical models exist to make predictions in a variety of physical contexts including materials science, fluid mechanics, and solid mechanics. Most of these models do not have known analytical solutions. Moreover, they are generally difficult to solve numerically. In order to perform numerically tractable computations, researchers often try to develop reduced models that account for the essential physics while doing away with the complexity of the full model. Development of reduced models necessarily...

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Non-Parametric Bayesian Techniques for Spatial Temporal Models, Optimisation and Decision Making

Event Date: Nov 02, 2017 in Seminario Aprendizaje de Máquinas, Seminars

Abstract: The use of Bayesian techniques for modelling spatial temporal phenomena has extensively increased over the last decade, providing flexibility and uncertainty quantification for inference and prediction. This talk focuses on how to place Gaussian Process models over complex phenomena and explores how the information from these models can be used for flexible uncertainty aware decision making. This talk provides examples of the application and advantages of using these techniques for environmental monitoring, quantitative social sciences, criminology and human behaviour.   (Esta...

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Variational Inference

Event Date: Nov 30, 1999 in Seminario Aprendizaje de Máquinas, Seminars

Abstract: Bayesian methods have shown to be very successful and attractive approaches in Machine Learning, thanks to the natural representation of uncertainty in real problems, the automatic control of overfitting, and the intuitive modeling of semantics through the use of latent variables and graphical models. In most cases, Bayesian approaches have to deal with the inference of posterior probabilities of latent variables given data, in order to make predictions for future cases or to perform model selection as well. Unfortunately, in most of the real cases, the estimation of those...

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