Publications related to Total variation distance of probability measures

Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences

Michael Christoph Gastpar, Adrien Vandenbroucque, Amedeo Roberto Esposito

We consider the problem of parameter estimation in a Bayesian setting and propose a general lower-bound that includes part of the family of f-Divergences. The results are then applied to specific settings of interest and compared to other notable results i ...

2022

Locally Differentially-Private Randomized Response for Discrete Distribution Learning

Michael Christoph Gastpar, Adriano Pastore

We consider a setup in which confidential i.i.d. samples X1, . . . , Xn from an unknown finite-support distribution p are passed through n copies of a discrete privatization chan- nel (a.k.a. mechanism) producing outputs Y1, . . . , Yn. The channel law gua ...

2021

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Nicolas Henri Bernard Flammarion, Xiang Cheng

We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as optimization on the space of probability measures, with Kullback-Leibler (KL) divergence as the objective functional. We show that an under-damped form of the Langevin algorithm perfor ...

INT STATISTICAL INST2021

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

In this work, the probability of an event under some joint distribution is bounded by measuring it with the product of the marginals instead (which is typically easier to analyze) together with a measure of the dependence between the two random variables. ...

2021

Robust Generalization via f−Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

Given two probability measures P and Q and an event E, we provide bounds on P(E) in terms of Q(E) and f-divergences. In particular, the bounds are instantiated when the measures considered are a joint distribution and the corresponding product of marginals ...

IEEE2020

Robust Generalization via $\alpha$-Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

The aim of this work is to provide bounds connecting two probability measures of the same event using Rényi

\alpha

-Divergences and Sibson’s

\alpha

-Mutual Information, a generalization of respectively the Kullback-Leibler Divergence and Shannon’s Mutual ...

ETHZ2020

Improving the arm-hand coordination in neuroprosthetics control with prior information from muscle activity

Iason Batzianoulis

Humans use their hands mainly for grasping and manipulating objects, performing simple and dexterous tasks. The loss of a hand may significantly affect one's working status and independence in daily life. A restoration of the grasping ability is important ...

EPFL2019

Penalization and Bayesian numerical methods for multiscale inverse problems

Andrea Di Blasio

In this thesis we consider inverse problems involving multiscale elliptic partial differential equations. The name multiscale indicates that these models are characterized by the presence of parameters which vary on different spatial scales (macroscopic, m ...

EPFL2019

Distribution-Matching Embedding for Visual Domain Adaptation

Mathieu Salzmann

Domain-invariant representations are key to addressing the domain shift problem where the training and test examples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typical ...

2016

Rehabilitation of Count-based Models for Word Vector Representations

Rémi Philippe Lebret, Ronan Collobert

Recent works on word representations mostly rely on predictive models. Distributed word representations (aka word embeddings) are trained to optimally predict the contexts in which the corresponding words tend to appear. Such models have succeeded in captu ...

Springer International Publishing2015

Total variation distance of probability measures

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Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences

Locally Differentially-Private Randomized Response for Discrete Distribution Learning

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Robust Generalization via f−Mutual Information

Robust Generalization via $\alpha$-Mutual Information

Improving the arm-hand coordination in neuroprosthetics control with prior information from muscle activity

Penalization and Bayesian numerical methods for multiscale inverse problems

Distribution-Matching Embedding for Visual Domain Adaptation

Rehabilitation of Count-based Models for Word Vector Representations

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences

Distribution-Matching Embedding for Visual Domain Adaptation

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Locally Differentially-Private Randomized Response for Discrete Distribution Learning

Rehabilitation of Count-based Models for Word Vector Representations

Robust Generalization via f−Mutual Information

Robust Generalization via $\alpha$-Mutual Information

Improving the arm-hand coordination in neuroprosthetics control with prior information from muscle activity

Penalization and Bayesian numerical methods for multiscale inverse problems