Concept

Bregman divergence

Related publications (62)

Locally differentially-private distribution estimation

Michael Christoph Gastpar, Adriano Pastore

We consider a setup in which confidential i.i.d. samples X1, ..., Xn from an unknown discrete distribution PX are passed through a discrete memoryless privatization channel (a.k.a. mechanism) which guarantees an epsilon-level of local differential privacy. ...

Ieee2016

Optimality of Operator-Like Wavelets for Representing Sparse AR(1) Processes

Michaël Unser, Pedram Pad

The discrete cosine transform (DCT) is known to be asymptotically equivalent to the Karhunen-Loève transform (KLT) of Gaussian first-order auto-regressive (AR(1)) processes. Since being uncorrelated under the Gaussian hypothesis is synonymous with independ ...

IEEE2015

Distributed inference over regression and classification models

Ali H. Sayed

We study the distributed inference task over regression and classification models where the likelihood function is strongly log-concave. We show that diffusion strategies allow the KL divergence between two likelihood functions to converge to zero at the r ...

IEEE2013

Analytic Sensing For Multi-Layer Spherical Models With Application To EEG Source Imaging

Dimitri Nestor Alice Van De Ville, Thierry Blu, Djano Kandaswamy

Source imaging maps back boundary measurements to underlying generators within the domain; e. g., retrieving the parameters of the generating dipoles from electrical potential measurements on the scalp such as in electroencephalography (EEG). Fitting such ...

Amer Inst Mathematical Sciences2013

KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization

Ilya Loshchilov

This paper investigates the control of an ML component within the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) devoted to black-box optimization. The known CMA-ES weakness is its sample complexity, the number of evaluations of the objective fun ...

2013

Enhancing State Mapping-Based Cross-Lingual Speaker Adaptation using Phonological Knowledge in a Data-Driven Manner

John David Scott Dines, Hui Liang

HMM state mapping with the Kullback-Leibler divergence as a distribution similarity measure is a simple and effective technique that enables cross-lingual speaker adaptation for speech synthesis. However, since this technique does not take any other potent ...

Idiap2013

Lower and upper bounds for approximation of the Kullback-Leibler divergence between Gaussian Mixture Models

Jean-Philippe Thiran, Finnian Paul Kelly

Many speech technology systems rely on Gaussian Mixture Models (GMMs). The need for a comparison between two GMMs arises in applications such as speaker verification, model selection or parameter estimation. For this purpose, the Kullback-Leibler (KL) dive ...

Ieee2012

Sizing Studies for Detecting Graphical Models

Stephan Morgenthaler, Maya Shevlyakova

The ability to identify reliably a positive or negative partial correlation between the expression levels of two genes is determined by the number p of genes, the number n of analyzed samples, and the statistical properties of the measurements. Classical s ...

EPFL STAP2012

COMBINING CEPSTRAL NORMALIZATION AND COCHLEAR IMPLANT-LIKE SPEECH PROCESSING FOR MICROPHONE ARRAY-BASED SPEECH RECOGNITION

Philip Neil Garner, Mohammadjavad Taghizadeh

This paper investigates the combination of cepstral normalization and cochlear implant-like speech processing for microphone array- based speech recognition. Testing speech signals are recorded by a circular microphone array and are subsequently processed ...

2012

Accounting for Observational Uncertainty in Forecast Verification: An Information-Theoretical View on Forecasts, Observations, and Truth

Steven Vincent Weijs, Nicolaas Cornelis van de Giesen

Recently, an information-theoretical decomposition of Kullback–Leibler divergence into uncertainty, reliability, and resolution was introduced. In this article, this decomposition is generalized to the case where the observation is uncertain. Along with a ...

2011

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