Publications related to Estimation of Conditional Distributions using Gaussian Mixture Models

Density Estimation In Rkhs With Application To Korobov Spaces In High Dimensions

A kernel method for estimating a probability density function from an independent and identically distributed sample drawn from such density is presented. Our estimator is a linear combination of kernel functions, the coefficients of which are determined b ...

SIAM PUBLICATIONS2023

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

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

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

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

Wasserstein Distributionally Robust Learning

Soroosh Shafieezadeh Abadeh

Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...

EPFL2020

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Fei Pu

In this thesis, we study systems of linear and/or non-linear stochastic heat equations and fractional heat equations in spatial dimension

1

driven by space-time white noise. The main topic is the study of hitting probabilities for the solutions to these ...

EPFL2018

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Pascal Fua, François Fleuret

Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probabilit ...

Ieee2017

Estimation of Conditional Distributions using Gaussian Mixture Models

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Density Estimation In Rkhs With Application To Korobov Spaces In High Dimensions

Geometrically-Conditioned Point Diffusion Models

A Wasserstein-based measure of conditional dependence

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

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

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

Wasserstein Distributionally Robust Learning

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Density Estimation In Rkhs With Application To Korobov Spaces In High Dimensions

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

Geometrically-Conditioned Point Diffusion Models

Wasserstein Distributionally Robust Learning

A Wasserstein-based measure of conditional dependence

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

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

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Multi-Modal Mean-Fields via Cardinality-Based Clamping