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Accurate Directional Inference for Vector Parameters in Linear Exponential Families

Publications associées (54)

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Sparse Probabilistic Classifiers

The scores returned by support vector machines are often used as a confidence measures in the classification of new examples. However, there is no theoretical argument sustaining this practice. Thus, when classification uncertainty has to be assessed, it i ...

2007

Sparse Probabilistic Classifiers

IDIAP2007

The estimation of multivariate extreme value models from choice-based samples

Michel Bierlaire

We consider an estimation procedure for discrete choice models in general and Multivariate Extreme Value (MEV) models in particular. It is based on a pseudo-likelihood function, generalizing the Conditional Maximum Likelihood (CML) estimator by Manski and ...

2007

Bayesian Inference for Sparse Generalized Linear Models

Matthias Seeger, Matthias Bethge

We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The parameters can be endowed with a factorizing prior distribution, encoding properties ...

Springer2007

Numerical methods and models relevant to transportation applications

Michaël Clément Louis Ghislain Thémans

In this thesis, we focus on standard classes of problems in numerical optimization: unconstrained nonlinear optimization as well as systems of nonlinear equations. More precisely, we consider two types of unconstrained nonlinear optimization problems. On t ...

EPFL2007

Specification, estimation and validation of a pedestrian walking behavior model

Michel Bierlaire, Gianluca Antonini, Thomas Robin, Sabina Schneider

We propose and validate a model for pedestrian walking behavior, based on discrete choice modeling. Two main behaviors are identified: unconstrained and constrained. The constrained patterns are captured by a leader-follower model and by a collision avoida ...

2007

The estimation of Generalized Extreme Value models from choice-based samples

Michel Bierlaire

We consider an estimation procedure for discrete choice models in general and Generalized Extreme Value (GEV) models in particular. It is based on a pseudo-likelihood function, generalizing the Conditional Maximum Likelihood (CML) estimator by Manski and M ...

2006

Worst-Case Bounds for Gaussian Process Models

Matthias Seeger

We present a competitive analysis of some non-parametric Bayesian algorithms in a worst-case online learning setting, where no probabilistic assumptions about the generation of the data are made. We consider models which use a Gaussian process prior (over ...

2006

Robust estimation, interpretation and assessment of likelihood ratios in forensic speaker recognition

Andrzej Drygajlo

In this contribution, the Bayesian framework for interpretation of evidence when applied to forensic speaker recognition is introduced. Different aspects of the use of voice as evidence in the court are addressed, as well as the use by the forensic expert ...

2006

Capturing interactioons in pedestrian walking behavior in a discrete choice framework

Michel Bierlaire

In this paper we propose a general framework for pedestrian walking behavior, based on discrete choice modeling. Two main behaviors are identified: unconstrained and constrained. The constrained patterns are further classified into attractive interactions ...

STRC 20062006