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Generalized approximate survey propagation for high-dimensional estimation*

Related publications (32)

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Semi-supervised dimensionality reduction for analyzing high-dimensional data with constraints

Sofien Bouaziz

In this paper, we present a novel semi-supervised dimensionality reduction technique to address the problems of inefficient learning and costly computation in coping with high-dimensional data. Our method named the dual subspace projections (DSP) embeds hi ...

Elsevier2012

Chebyshev polynomial approximation for transductive learning on graphs

Nathanaël Perraudin

The goal of transductive learning is to find a way to recover the labels of lots of data with only a few known samples. In this work, we will work on graphs for two reasons. First, it’s possible to construct a graph from a given dataset with features. The ...

2011

Convex Variational Bayesian Inference for Large Scale Generalized Linear Models

Matthias Seeger

We show how variational Bayesian inference can be implemented for very large binary classification generalized linear models. Our relaxation is shown to be a convex problem for any log-concave model, and we provide an efficient double loop algorithm for so ...

2009

Accurate parametric inference for small samples

Anthony Christopher Davison

We outline how modern likelihood theory, which provides essentially exact inferences in a variety of parametric statistical problems, may routinely be applied in practice. Although the likelihood procedures are based on analytical asymptotic approximations ...

2008

Generalized linear prediction method in phase-shifting interferometry in the presence of noise

Pramod Rastogi, Abhijit Patil, Rajesh Langoju

The effectiveness of phase-shifting interferometry (PSI) techniques employing piezoelectric device PZT in the estimation of phase depends largely on the accuracy with which the phase shifts are imparted to the device and the noise influencing the measureme ...

Elsevier2007

Gaussian Process Belief Propagation

Matthias Seeger

The framework of graphical models is a cornerstone of applied Statistics, allowing for an intuitive graphical specification of the main features of a model, and providing a basis for general Bayesian inference computations though belief propagation (BP). I ...

MIT Press2007

Multivariate Nonlinear Regression with Semiparametric Latent Factor Models

Matthias Seeger

We propose a semiparametric model for regression problems involving multiple response variables. Conditional dependencies between the responses are represented through a linear mixture of Gaussian processes. We propose an efficient approximate inference sc ...

2005

Adapted Generative Models For Face Verification

Samy Bengio

It has been shown previously that systems based on local features and relatively complex generative models, namely 1D Hidden Markov Models (HMMs) and pseudo-2D HMMs, are suitable for face recognition (here we mean both identification and verification). Rec ...

IDIAP2003

Learning the Decision Function for Speaker Verification

Samy Bengio

This paper explores the possibility to replace the usual thresholding decision rule of log likelihood ratios used in speaker verification systems by more complex and discriminant decision functions based for instance on Linear Regression models or Support ...

2001

Learning the Decision Function for Speaker Verification

Samy Bengio

IDIAP2000