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Concept

Conditional independence

Publications associées (20)

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We give an extension of Le's stochastic sewing lemma. The stochastic sewing lemma proves convergence in

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for an adapted two-parameter stochastic process A, under certain conditions on the moments o ...

Cambridge Univ Press2024

Geometrically-Conditioned Point Diffusion Models

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Diffusion models generating images conditionally on text, such as Dall-E 2 [51] and Stable Diffusion[53], have recently made a splash far beyond the computer vision com- munity. Here, we tackle the related problem of generating point clouds, both unconditi ...

2023

A Wasserstein-based measure of conditional dependence

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Measuring conditional dependencies among the variables of a network is of great interest to many disciplines. This paper studies some shortcomings of the existing dependency measures in detecting direct causal influences or their lack of ability for group ...

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The Gray-Wyner Network and Wyner's Common Information for Gaussian Sources

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This paper presents explicit solutions for two related non-convex information extremization problems due to Gray and Wyner in the Gaussian case. The first problem is the Gray-Wyner network subject to a sum-rate constraint on the two private links. Here, ou ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Quantifying uncertain system outputs via the multi-level Monte Carlo method --- distribution and robustness measures

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In this work, we consider the problem of estimating the probability distribution, the quantile or the conditional expectation above the quantile, the so called conditional-value-at-risk, of output quantities of complex random differential models by the MLM ...

2023

Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network

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In high dimensional robotic system, the manifold of the valid configuration space often has a complex shape, especially under constraints such as end-effector orientation or static stability. We propose a generative adversarial network approach to learn th ...

IEEE2021

On quantifying the quality of acoustic models in hybrid DNN-HMM ASR

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We propose an information theoretic framework for quantitative assessment of acoustic models used in hidden Markov model (HMM) based automatic speech recognition (ASR). The HMM backend expects that (i) the acoustic model yields accurate state conditional e ...

ELSEVIER2020

Distributionally Robust Optimization with Polynomial Densities: Theory, Models and Algorithms

Daniel Kuhn

In distributionally robust optimization the probability distribution of the uncertain problem parameters is itself uncertain, and a fictitious adversary, e.g., nature, chooses the worst distribution from within a known ambiguity set. A common shortcoming o ...

2020

Active contraction of cardiac cells: a reduced model for sarcomere dynamics with cooperative interactions

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SPRINGER HEIDELBERG2018

Semiparametric Bayesian Risk Estimation for Complex Extremes

Thomas Lugrin

Extreme events are responsible for huge material damage and are costly in terms of their human and economic impacts. They strike all facets of modern society, such as physical infrastructure and insurance companies through environmental hazards, banking an ...

EPFL2018

Total Correlation of Gaussian Vector Sources on the Gray–Wyner Network

Michael Christoph Gastpar, Guillaume Jean Op 't Veld

We study a generalization of Wyner’s Common Information to Watanabe’s Total Correlation. The first minimizes the description size required for a variable that can make two other random variables conditionally independent. If independence is unattainable, W ...

2016

High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks

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We propose three modeling methods using a mobile sensor network to generate high spatio-temporal resolution air pollution maps for urban environments. In our deployment in Lausanne (Switzerland), dedicated sensing nodes are anchored to the public buses and ...

IEEE2015

Pattern-wise trust assessment of sensor data

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One of the most important tasks of a sensor network (SN) is to detect occurrences of interesting events in the monitored environment. However, data measured by SN is often affected by errors. We investigate the problem of assessing trustworthiness (trust) ...

2014

A bottom-up stochastic model to predict building occupants' time-dependent activities

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A bottom-up modelling approach together with a set of calibration methodologies is presented to predict residential building occupants' time-dependent activities, for use in dynamic building simulations. The stochastic model to predict activity chains is c ...

Elsevier2013

Conditional probabilities in the excursion set theory: generic barriers and non-Gaussian initial conditions

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The excursion set theory, where density perturbations evolve stochastically with the smoothing scale, provides a method for computing the dark matter halo mass function. The computation of the mass function is mapped into the so-called first-passage time p ...

Blackwell Publishing Ltd, Oxford2011

Bayesian networks and information theory for audio-visual perception modeling

Jonas Richiardi, Patricia Besson

Thanks to their different senses, human observers acquire multiple information coming from their environment. Complex cross-modal interactions occur during this perceptual process. This article proposes a framework to analyze and model these interactions t ...

2010

Closed-form solutions to stochastic switching problems

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This paper studies the price of an asset depending on both a fundamental and possible interventions of an authority. Using the martingale approach in continuous time, we provide closed-form solutions to switching problems involving irreversible, state depe ...

2008

A Class Of Mean Field Interaction Models for Computer and Communication Systems

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We consider models of N interacting objects, where the interaction is via a common resource and the distribution of states of all objects. We introduce the key scaling concept of intensity; informally, the expected number of transitions per object per time ...

2008

Speaker recognition in noisy environments using auxiliary information and Bayesian networks

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Speaker recognition systems achieve acceptable performance in controlled laboratory conditions. However, in real-life environments, the performance of a speaker recognition system degrades drastically, the principal cause being the mismatch that exists bet ...

EPFL2006

Conditionally least-informative distributions: The case of the location parameter

Stephan Morgenthaler

We extend a basic result of Huber's on least favorable distributions to the setting of conditional inference, using an approach based on the notion of log-Gâteaux differentiation and perturbed models. Whereas Huber considered intervals of fixed width for l ...

2005