Seminars

CURTIS-HEDLUND-LYNDON THEOREM FOR ULTRAGRAPH SHIFT SPACES

Event Date: Jan 22, 2018 in Dynamical Systems, Seminars

ABSTRACT: In this work we characterize the class of continuous shift commuting maps between ultragraph shift spaces, proving a Curtis-Hedlund-Lyndon type theorem. Then we use it to characterize continuous, shift commuting, length preserving maps in terms of generalized sliding block codes. This is a joint work with Prof. Daniel Gon\c{c}alves (UFSC, Brazil)

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Projected solutions of quasi-variational inequalities with application to bidding process in electricity market

Event Date: Jan 17, 2018 in Optimization and Equilibrium, Seminars

Abstract:   Quasi-variational inequalities provide perfect tools  to reformulate Generalized Nash Equilibrium Probem (GNEP), the latter being a good model to describe the day-ahead  electricity markets.   Our aim in this talk is to illustrate how some recent advances in the theory  of quasi-variational inequalities can influence the modeling of electricity market.   Talk based on: – D. Aussel, A. Sultana & V. Vetrivel, On the existence of projected solutions of quasi-variational inequalities and generalized Nash equilibrium problem, J. Optim. Th. Appl. 170 (2016),...

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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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Invariant measures of discrete interacting particle systems: Algebraic aspects

Event Date: Jan 09, 2018 in Núcleo Modelos Estocásticos de Sistemas Complejos y Desordenados, Seminars

Abstract: We consider a continuous time particle system on a graph L being either Z,  Z_n, a segment {1,…, n}, or Z^d, with state space Ek={0,…,k-1} for some k belonging to {infinity, 2, 3, …}. We also assume that the Markovian evolution is driven by some translation invariant local dynamics with bounded width dependence, encoded by a rate matrix T. These are standard settings, satisfied by many studied particle systems. We provide some sufficient and/or necessary conditions on the matrix T, so that this Markov process admits some simple invariant distribution, as a product...

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