Daniel Sanz-Alonso

,

CoRR, March, 2026

Convergence Rates for Learning Pseudo-Differential Operators.

[BibT_eX]

[DOI]

Jiaheng Chen

,

CoRR, January, 2026

Bayesian Optimization on Networks.

[BibT_eX]

[DOI]

Wenwen Li

,

,

CoRR, October, 2025

Functional Multi-Reference Alignment via Deconvolution.

[BibT_eX]

[DOI]

,

,

,

CoRR, June, 2025

Hierarchical Bayesian Inverse Problems: A High-Dimensional Statistics Viewpoint.

[BibT_eX]

[DOI]

,

Nathan Waniorek

SIAM Rev., 2025

Gaussian Process Regression under Computational and Epistemic Misspecification.

[BibT_eX]

[DOI]

,

SIAM J. Numer. Anal., 2025

Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration.

[BibT_eX]

[DOI]

,

SIAM/ASA J. Uncertain. Quantification, 2025

Precision and Cholesky Factor Estimation for Gaussian Processes.

[BibT_eX]

[DOI]

Jiaheng Chen

,

SIAM/ASA J. Uncertain. Quantification, 2025

Optimization on Manifolds via Graph Gaussian Processes.

[BibT_eX]

[DOI]

,

,

SIAM J. Math. Data Sci., March, 2024

Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement.

[BibT_eX]

[DOI]

,

Nathan Waniorek

SIAM/ASA J. Uncertain. Quantification, March, 2024

Ensemble Kalman Filters with Resampling.

[BibT_eX]

[DOI]

Omar Al Ghattas

,

Jiajun Bao

,

SIAM/ASA J. Uncertain. Quantification, 2024

Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems.

[BibT_eX]

[DOI]

,

Nathan Waniorek

CoRR, 2024

Inverse Problems and Data Assimilation: A Machine Learning Approach.

[BibT_eX]

[DOI]

,

,

,

CoRR, 2024

A First Course in Monte Carlo Methods.

[BibT_eX]

[DOI]

,

Omar Al Ghattas

CoRR, 2024

Data Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet.

[BibT_eX]

[DOI]

Melissa Adrian

,

,

CoRR, 2024

Bayesian Optimization with Noise-Free Observations: Improved Regret Bounds via Random Exploration.

[BibT_eX]

[DOI]

,

CoRR, 2024

Reduced-Order Autodifferentiable Ensemble Kalman Filters.

[BibT_eX]

[DOI]

,

,

CoRR, 2023

Autodifferentiable Ensemble Kalman Filters.

[BibT_eX]

[DOI]

,

,

SIAM J. Math. Data Sci., June, 2022

Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy.

[BibT_eX]

[DOI]

,

SIAM/ASA J. Uncertain. Quantification, 2022

A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors.

[BibT_eX]

[DOI]

,

,

,

SIAM/ASA J. Uncertain. Quantification, 2022

Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective.

[BibT_eX]

[DOI]

,

J. Mach. Learn. Res., 2022

Non-Asymptotic Analysis of Ensemble Kalman Updates: Effective Dimension and Localization.

[BibT_eX]

[DOI]

Omar Al Ghattas

,

CoRR, 2022

Mathematical Foundations of Graph-Based Bayesian Semi-Supervised Learning.

[BibT_eX]

[DOI]

,

,

CoRR, 2022

Hierarchical Ensemble Kalman Methods with Sparsity-Promoting Generalized Gamma Hyperpriors.

[BibT_eX]

[DOI]

,

,

Alexander Strang

CoRR, 2022

HMC: Reducing the number of rejections by not using leapfrog and some results on the acceptance rate.

[BibT_eX]

[DOI]

Mari Paz Calvo

,

,

Jesús María Sanz-Serna

J. Comput. Phys., 2021

Bayesian Update with Importance Sampling: Required Sample Size.

[BibT_eX]

[DOI]

,

Zijian Wang

Entropy, 2021

Auto-differentiable Ensemble Kalman Filters.

[BibT_eX]

[DOI]

,

,

CoRR, 2021

Graph-based Prior and Forward Models for Inverse Problems on Manifolds with Boundaries.

[BibT_eX]

[DOI]

,

,

,

CoRR, 2021

Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds.

[BibT_eX]

[DOI]

John Harlim

,

,

SIAM/ASA J. Uncertain. Quantification, 2020

On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms.

[BibT_eX]

[DOI]

,

Zachary Kaplan

,

Thabo Samakhoana

,

J. Mach. Learn. Res., 2020

Iterative Ensemble Kalman Methods: A Unified Perspective with Some New Variants.

[BibT_eX]

[DOI]

Neil K. Chada

,

,

CoRR, 2020

The SPDE Approach to Matérn Fields: Graph Representations.

[BibT_eX]

[DOI]

,

CoRR, 2020

Data-Driven Forward Discretizations for Bayesian Inversion.

[BibT_eX]

[DOI]

Daniele Bigoni

,

,

,

Youssef M. Marzouk

,

CoRR, 2020

Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis.

[BibT_eX]

[DOI]

,

,

J. Mach. Learn. Res., 2019

Variational Characterizations of Local Entropy and Heat Regularization in Deep Learning.

[BibT_eX]

[DOI]

,

Zachary Kaplan

,

Entropy, 2019

HMC: avoiding rejections by not using leapfrog and some results on the acceptance rate.

[BibT_eX]

[DOI]

Mari Paz Calvo

,

,

Jesús María Sanz-Serna

CoRR, 2019

Continuum Limits of Posteriors in Graph Bayesian Inverse Problems.

[BibT_eX]

[DOI]

,

SIAM J. Math. Anal., 2018

Importance Sampling and Necessary Sample Size: An Information Theory Approach.

[BibT_eX]

[DOI]

SIAM/ASA J. Uncertain. Quantification, 2018

On the Consistency of Graph-based Bayesian Learning and the Scalability of Sampling Algorithms.

[BibT_eX]

[DOI]

,

Zachary Kaplan

,

Thabo Samakhoana

,

CoRR, 2017

Long-Time Asymptotics of the Filtering Distribution for Partially Observed Chaotic Dynamical Systems.

[BibT_eX]

[DOI]