Publications related to Conditional independence

An extension of the stochastic sewing lemma and applications to fractional stochastic calculus

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

L_m

of Riemann type sums

\sum _{[s,t] \in \pi } A_{s,t}

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

Cambridge Univ Press2024

Geometrically-Conditioned Point Diffusion Models

Pascal Fua, Eduard Trulls Fortuny, Michal Jan Tyszkiewicz

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

Negar Kiyavash, Seyed Jalal Etesami, Kun Zhang

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 ...

2022

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

Michael Christoph Gastpar, Erixhen Sula

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

Fabio Nobile, Sebastian Krumscheid, Sundar Subramaniam Ganesh

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 ...

2022

Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network

Sylvain Calinon, Julius Maximilian Jankowski, Emmanuel Pignat, Teguh Santoso Lembono

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

Hervé Bourlard, Afsaneh Asaei, Pranay Dighe

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

Conditional separable effects

Mats Julius Stensrud, Aaron Leor Sarvet

Researchers are often interested in treatment effects on outcomes that are only defined conditional on a post-treatment event status. For example, in a study of the effect of different cancer treatments on quality of life at end of follow-up, the quality o ...

2020

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

Alfio Quarteroni, Francesco Regazzoni

We propose a reduced ODE model for the mechanical activation of cardiac myofilaments, which is based on explicit spatial representation of nearest-neighbour interactions. Our model is derived from the cooperative Markov Chain model of Washio et al. (Cell M ...

SPRINGER HEIDELBERG2018

Conditional independence

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An extension of the stochastic sewing lemma and applications to fractional stochastic calculus

Geometrically-Conditioned Point Diffusion Models

A Wasserstein-based measure of conditional dependence

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

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

Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network

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

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

Conditional separable effects

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

A Wasserstein-based measure of conditional dependence

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

Conditional separable effects

Geometrically-Conditioned Point Diffusion Models

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

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

An extension of the stochastic sewing lemma and applications to fractional stochastic calculus

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

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

Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network