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Efficient learning of smooth probability functions from Bernoulli tests with guarantees.

Related publications (32)

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Solving Stochastic AC Power Flow via Polynomial Chaos Expansion

Timm Faulwasser

The present contribution demonstrates the applicability of polynomial chaos expansion to stochastic (optimal) AC power flow problems that arise in the operation of power grids. For rectangular power flow, polynomial chaos expansion together with Galerkin p ...

IEEE2016

MMSE Estimation of Sparse Levy Processes

Michaël Unser, Arash Amini, Ulugbek Kamilov, Pedram Pad

We investigate a stochastic signal-processing framework for signals with sparse derivatives, where the samples of a Levy process are corrupted by noise. The proposed signal model covers the well-known Brownian motion and piecewise-constant Poisson process; ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Stratégies de compromis pour l'estimation des paramètres de régression et pour la classification floue

Nicolas Fournier

Model specification is an integral part of any statistical inference problem. Several model selection techniques have been developed in order to determine which model is the best one among a list of possible candidates. Another way to deal with this questi ...

EPFL2011

Creating Probabilistic Databases from Imprecise Time-Series Data

Karl Aberer, Saket Sathe, Ho Young Jeung

Although efficient processing of probabilistic databases is a well-established field, a wide range of applications are still unable to benefit from these techniques due to the lack of means for creating probabilistic databases. In fact, it is a challenging ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Convexity bounds for BSDE solutions, with applications to indifference valuation

Christoph Frei, Semyon Malamud

We consider backward stochastic differential equations (BSDEs) with a particular quadratic generator and study the behaviour of their solutions when the probability measure is changed, the filtration is shrunk, or the underlying probability space is transf ...

2011

Support Vector Machines with a Reject Option

We consider the problem of binary classification where the classfier may abstain instead of classifying each observation. The Bayes decision rule for this setup, known as Chow’s rule, is deﬁned by two thresholds on posterior probabilities. From simple desi ...

Idiap2009

Convexity bounds for BSDE solutions, with applications to indiﬀerence valuation

Christoph Frei, Semyon Malamud

We consider backward stochastic diﬀerential equations (BSDEs) with a particular quadratic generator and study the behaviour of their solu- tions when the probability measure is changed, the ﬁltration is shrunk, or the underlying probability space is transf ...

2009

Using KL-based Acoustic Models in a Large Vocabulary Recognition Task

Hervé Bourlard, Guillermo Aradilla

Posterior probabilities of sub-word units have been shown to be an effective front-end for ASR. However, attempts to model this type of features either do not benefit from modeling context-dependent phonemes, or use an inefficient distribution to estimate ...

IDIAP2008

Support Vector Machines with a Reject Option

We consider the problem of binary classification where the classifier may abstain instead of classifying each observation. The Bayes decision rule for this setup, known as Chow’s rule, is deﬁned by two thresholds on posterior probabilities. From simple des ...

2008

Comparing Different Word Lattice Rescoring Approaches Towards Keyword Spotting

Hervé Bourlard, Hynek Hermansky, Joel Praveen Pinto

In this paper, we further investigate the large vocabulary continuous speech recognition approach to keyword spotting. Given a speech utterance, recognition is performed to obtain a word lattice. The posterior probability of keyword hypotheses in the latti ...

IDIAP2007