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05/10/2023
Principal Feature Detection via Φ-Sobolev Inequalities
We investigate the approximation of high-dimensional target measures as ...
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03/05/2023
Self-reinforced polynomial approximation methods for concentrated probability densities
Transport map methods offer a powerful statistical learning tool that ca...
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07/18/2022
Gradient-based data and parameter dimension reduction for Bayesian models: an information theoretic perspective
We consider the problem of reducing the dimensions of parameters and dat...
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06/08/2021
Conditional Deep Inverse Rosenblatt Transports
We present a novel offline-online method to mitigate the computational b...
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02/26/2021
Data-Free Likelihood-Informed Dimension Reduction of Bayesian Inverse Problems
Identifying a low-dimensional informed parameter subspace offers a viabl...
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02/20/2021
Nonlinear dimension reduction for surrogate modeling using gradient information
We introduce a method for the nonlinear dimension reduction of a high-di...
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01/08/2021
Learning non-Gaussian graphical models via Hessian scores and triangular transport
Undirected probabilistic graphical models represent the conditional depe...
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09/22/2020
An adaptive transport framework for joint and conditional density estimation
We propose a general framework to robustly characterize joint and condit...
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10/25/2019
Randomized residual-based error estimators for the Proper Generalized Decomposition approximation of parametrized problems
This paper introduces a novel error estimator for the Proper Generalized...
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05/31/2019
Greedy inference with layers of lazy maps
We propose a framework for the greedy approximation of high-dimensional ...
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07/02/2018