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Bayesian adaptation of chaos representations using variational inference and sampling on geodesics

Publications associées (60)

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Bayesian Calibration of Thermal-Hydraulics Model with Time-Dependent Output

Andreas Pautz, Damar Canggih Wicaksono, Omar Zerkak

The currently adopted practice for uncertainty quantification of thermal-hydraulics code predictions is done through statistical sampling where the code is evaluated multiple times using different values of input parameters that are randomly generated acco ...

2016

A block algorithm for computing antitriangular factorizations of symmetric matrices

Daniel Kressner, Zvonimir Bujanovic

Any symmetric matrix can be reduced to antitriangular form in finitely many steps by orthogonal similarity transformations. This form reveals the inertia of the matrix and has found applications in, e.g., model predictive control and constraint preconditio ...

Springer2016

We analyze the accuracy of the discrete least-squares approximation of a function u in multivariate polynomial spaces

P_\Lambda := span\{y \mapsto y^\nu | \nu \in \Lambda\}

with

\Lambda\subset N_0^d

over the domain

\Gamma := [-1,1]^d

, based on the sa ...

MATHICSE2015

Kullback-Leibler Proximal Variational Inference

Pascal Fua, François Fleuret, Pierre Bruno Baqué, Mohammad Emtiyaz Khan

We propose a new variational inference method based on a proximal framework that uses the Kullback-Leibler (KL) divergence as the proximal term. We make two contributions towards exploiting the geometry and structure of the variational bound. Firstly, we p ...

2015

Bilinear models of count data with Poisson distribution are popular in applications such as matrix factorization for recommendation systems, modeling of receptive fields of sensory neurons, and modeling of neural-spike trains. Bayesian inference in such mo ...

2014

Linear Bayesian Reinforcement Learning

Christos Dimitrakakis, Nikolaos Tziortziotis

This paper proposes a simple linear Bayesian approach to reinforcement learning. We show that with an appropriate basis, a Bayesian linear Gaussian model is sufficient for accurately estimating the system dynamics, and in particular when we allow for corre ...

2013

Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models

Matthias Seeger, Mohammad Emtiyaz Khan

Latent Gaussian models (LGMs) are widely used in statistics and machine learning. Bayesian inference in non-conjugate LGMs is difficult due to intractable integrals in- volving the Gaussian prior and non-conjugate likelihoods. Algorithms based on variation ...

Omni Press2013

Robust and Private Bayesian Inference

Christos Dimitrakakis, Aikaterini Mitrokotsa

We examine the robustness and privacy properties of Bayesian inference under assumptions on the prior, but without any modifications to the Bayesian framework. First, we generalise the concept of differential privacy to arbitrary dataset distances, outcome ...

2013

When system identification methodologies are used to interpret measurement data taken from structures, uncertainty dependencies are in many cases unknown due to model simplifications and omissions. This paper presents how error-domain model falsification r ...

Elsevier2013

Probabilistic Depth Image Registration incorporating Nonvisual Information

Aude Billard, Ludovic Righetti

In this paper, we derive a probabilistic registration algorithm for object modeling and tracking. In many robotics applications, such as manipulation tasks, nonvisual information about the movement of the object is available, which we will combine with the ...

Ieee2012

We analyze the accuracy of the discrete least-squares approximation of a function u in multivariate polynomial spaces

P_\Lambda := span\{y \mapsto y^\nu | \nu \in \Lambda\}

with

\Lambda\subset N_0^d

over the domain

\Gamma := [-1,1]^d

, based on the sa ...

MATHICSE2015