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09/03/2023
Distribution learning via neural differential equations: a nonparametric statistical perspective
Ordinary differential equations (ODEs), via their induced flow maps, pro...
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08/26/2023
A transport approach to sequential simulation-based inference
We present a new transport-based approach to efficiently perform sequent...
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08/22/2023
Evaluating the accuracy of Gaussian approximations in VSWIR imaging spectroscopy retrievals
The joint retrieval of surface reflectances and atmospheric parameters i...
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07/23/2023
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices
We introduce a multifidelity estimator of covariance matrices formulated...
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05/16/2023
A score-based operator Newton method for measure transport
Transportation of probability measures underlies many core tasks in stat...
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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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04/01/2023
Diffusion map particle systems for generative modeling
We propose a novel diffusion map particle system (DMPS) for generative m...
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02/20/2023
Infinite-Dimensional Diffusion Models for Function Spaces
We define diffusion-based generative models in infinite dimensions, and ...
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01/31/2023
Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry
We introduce a multi-fidelity estimator of covariance matrices that empl...
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10/31/2022
Ensemble transport smoothing – Part 2: nonlinear updates
Smoothing is a specialized form of Bayesian inference for state-space mo...
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10/31/2022
Ensemble transport smoothing – Part 1: unified framework
Smoothers are algorithms for Bayesian time series re-analysis. Most oper...
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07/20/2022
On minimax density estimation via measure transport
We study the convergence properties, in Hellinger and related distances,...
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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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03/10/2022
A low-rank ensemble Kalman filter for elliptic observations
We propose a regularization method for ensemble Kalman filtering (EnKF) ...
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12/01/2021
hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty
Bayesian inference provides a systematic framework for integration of da...
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11/13/2021
Computing f-Divergences and Distances of High-Dimensional Probability Density Functions – Low-Rank Tensor Approximations
Very often, in the course of uncertainty quantification tasks or data an...
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07/28/2021
Sparse approximation of triangular transports. Part II: the infinite dimensional case
For two probability measures ρ and π on [-1,1]^ℕ we investigate the appr...
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05/14/2021
Bayesian inference under model misspecification using transport-Lagrangian distances: an application to seismic inversion
Model misspecification constitutes a major obstacle to reliable inferenc...
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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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01/18/2021
A Koopman framework for rare event simulation in stochastic differential equations
We exploit the relationship between the stochastic Koopman operator and ...
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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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06/12/2020
Sparse approximation of triangular transports on bounded domains
Let ρ and π be two probability measures on [-1,1]^d with positive and an...
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06/11/2020
Conditional Sampling With Monotone GANs
We present a new approach for sampling conditional measures that enables...
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06/03/2020
Batch greedy maximization of non-submodular functions: Guarantees and applications to experimental design
We propose and analyze batch greedy heuristics for cardinality constrain...
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05/29/2020
Rate-optimal refinement strategies for local approximation MCMC
Many Bayesian inference problems involve target distributions whose dens...
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04/25/2020
Low-rank multi-parametric covariance identification
We propose a differential geometric construction for families of low-ran...
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03/18/2020
Data-Driven Forward Discretizations for Bayesian Inversion
This paper suggests a framework for the learning of discretizations of e...
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01/06/2020
Geodesically parameterized covariance estimation
Statistical modeling of spatiotemporal phenomena often requires selectin...
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06/30/2019
Coupling techniques for nonlinear ensemble filtering
We consider filtering in high-dimensional non-Gaussian state-space model...
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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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03/03/2019
Scalable optimization-based sampling on function space
Optimization-based samplers provide an efficient and parallellizable app...
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08/28/2018
A transport-based multifidelity preconditioner for Markov chain Monte Carlo
Markov chain Monte Carlo (MCMC) sampling of posterior distributions aris...
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07/02/2018
Certified dimension reduction in nonlinear Bayesian inverse problems
We propose a dimension reduction technique for Bayesian inverse problems...
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06/08/2018
A Stein variational Newton method
Stein variational gradient descent (SVGD) was recently proposed as a gen...
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11/02/2017
Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting
We present an algorithm to identify sparse dependence structure in conti...
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05/12/2017
Exploiting network topology for large-scale inference of nonlinear reaction models
The development of chemical reaction models aids system design and optim...
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03/17/2017
