Concept

Loi de probabilité marginale

Publications associées (159)

Robust Generalization via $\alpha$-Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

The aim of this work is to provide bounds connecting two probability measures of the same event using Rényi

\alpha

-Divergences and Sibson’s

\alpha

-Mutual Information, a generalization of respectively the Kullback-Leibler Divergence and Shannon’s Mutual ...

ETHZ2020

Partial safety factor format for the resistance of structural concrete considering multiple failure modes

Aurelio Muttoni, Qianhui Yu, Miguel Fernández Ruiz

In this paper, the applicability of the Partial Safety Factor Format (PSFF) of EN1992-1-1:2004 (Eurocode 2 for Concrete Structures) to the reliability verification of structural concrete resistance involving multiple failure modes is investigated. This is ...

2020

Context Mover's Distance & Barycenters: Optimal Transport of Contexts for Building Representations

Martin Jaggi, Aymeric Daphnis Kévin Dieuleveut, Sidak Pal Singh, Andreas Hug

We present a framework for building unsupervised representations of entities and their compositions, where each entity is viewed as a probability distribution rather than a vector embedding. In particular, this distribution is supported over the contexts w ...

ADDISON-WESLEY PUBL CO2020

Floating bridges and various methods for determining their long-term extreme response due to wave loading

This project presents the theoretical background for calculating the long-term extreme response of pontoon-style floating bridges. Research into surrogate models for the long-term extreme response is given special attention, and various simulations of the ...

2020

Robust Generalization via f−Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

Given two probability measures P and Q and an event E, we provide bounds on P(E) in terms of Q(E) and f-divergences. In particular, the bounds are instantiated when the measures considered are a joint distribution and the corresponding product of marginals ...

IEEE2020

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Serge Vaudenay

The maximal achievable advantage of a (computationally unbounded) distinguisher to determine whether a source

Z

is distributed according to distribution

P_0

P_1

, when given access to one sample of

Z

, is characterized by the statistical distance ...

Springer, Cham2020

Extremal behaviour of aggregated data with an application to downscaling

Sebastian Engelke, Raphaël Gérard Théodore Michel Marie de Deloÿe et Fourcade de Fondeville

The distribution of spatially aggregated data from a stochastic process may exhibit tail behaviour different from that of its marginal distributions. For a large class of aggregating functionals we introduce the -extremal coefficient, which quantifies this ...

OXFORD UNIV PRESS2019

Adaptive learning for disruption prediction in non-stationary conditions

Olivier Sauter, Yiming Li, Ambrogio Fasoli, Basil Duval, Jonathan Graves, Duccio Testa, Patrick Blanchard, Alessandro Pau, Federico Alberto Alfredo Felici, Cristian Sommariva, Antoine Pierre Emmanuel Alexis Merle, Haomin Sun, Michele Marin, Henri Weisen, Richard Pitts, Yann Camenen, Jan Horacek, Javier García Hernández, Marco Wischmeier, Nicola Vianello, Mikhail Maslov, Federico Nespoli, Yao Zhou, Davide Galassi, Antonio José Pereira de Figueiredo, Hamish William Patten, Samuel Lanthaler, Emiliano Fable, Francesca Maria Poli, Daniele Brunetti, Anna Teplukhina, Alberto Mariani, Kenji Tanaka, Bernhard Sieglin, Otto Asunta, Gergely Papp, Leonardo Pigatto

For many years, machine learning tools have proved to be very powerful disruption predictors in tokamaks. On the other hand, the vast majority of the techniques deployed assume that the input data is independent and is sampled from exactly the same probabi ...

2019

A simple landscape of metastable state energies for two-dimensional cellular matter

Sangwoo Kim

The mechanical behavior of cellular matter in two dimensions can be inferred from geometric information near its energetic ground state. Here it is shown that the much larger set of all metastable state energies is universally described by a systematic exp ...

2019

Fast Automatic Smoothing for Generalized Additive Models

Anthony Christopher Davison, Yousra El Bachir

Generalized additive models (GAMs) are regression models wherein parameters of probability distributions depend on input variables through a sum of smooth functions, whose degrees of smoothness are selected by L-2 regularization. Such models have become th ...

MICROTOME PUBL2019

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