Long-Time Asymptotics of the Filtering Distribution for Partially Observed Chaotic Dynamical Systems. - Daniel Sanz-Alonso; Andrew M. Stuart (SIAM/ASA J. Uncertain. Quantification, 2015)

On the Consistency of Graph-based Bayesian Learning and the Scalability of Sampling Algorithms. - Nicolás García Trillos; Zachary Kaplan; Thabo Samakhoana; Daniel Sanz-Alonso (CoRR, 2017)

Importance Sampling and Necessary Sample Size: An Information Theory Approach. - Daniel Sanz-Alonso (SIAM/ASA J. Uncertain. Quantification, 2018)

Continuum Limits of Posteriors in Graph Bayesian Inverse Problems. - Nicolás García Trillos; Daniel Sanz-Alonso (SIAM J. Math. Anal., 2018)

HMC: avoiding rejections by not using leapfrog and some results on the acceptance rate. - Mari Paz Calvo; Daniel Sanz-Alonso; Jesús María Sanz-Serna (CoRR, 2019)

Variational Characterizations of Local Entropy and Heat Regularization in Deep Learning. - Nicolás García Trillos; Zachary Kaplan; Daniel Sanz-Alonso (Entropy, 2019)

Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis. - Nicolás García Trillos; Daniel Sanz-Alonso; Ruiyi Yang (J. Mach. Learn. Res., 2019)

Data-Driven Forward Discretizations for Bayesian Inversion. - Daniele Bigoni; Yuming Chen; Nicolás García Trillos; Youssef M. Marzouk; Daniel Sanz-Alonso (CoRR, 2020)

The SPDE Approach to Matérn Fields: Graph Representations. - Daniel Sanz-Alonso; Ruiyi Yang (CoRR, 2020)

Iterative Ensemble Kalman Methods: A Unified Perspective with Some New Variants. - Neil K. Chada; Yuming Chen; Daniel Sanz-Alonso (CoRR, 2020)

On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms. - Nicolás García Trillos; Zachary Kaplan; Thabo Samakhoana; Daniel Sanz-Alonso (J. Mach. Learn. Res., 2020)

Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds. - John Harlim; Daniel Sanz-Alonso; Ruiyi Yang (SIAM/ASA J. Uncertain. Quantification, 2020)

Graph-based Prior and Forward Models for Inverse Problems on Manifolds with Boundaries. - John Harlim; Shixiao W. Jiang; Hwanwoo Kim; Daniel Sanz-Alonso (CoRR, 2021)

Auto-differentiable Ensemble Kalman Filters. - Yuming Chen; Daniel Sanz-Alonso; Rebecca Willett (CoRR, 2021)

Bayesian Update with Importance Sampling: Required Sample Size. - Daniel Sanz-Alonso; Zijian Wang (Entropy, 2021)

HMC: Reducing the number of rejections by not using leapfrog and some results on the acceptance rate. - Mari Paz Calvo; Daniel Sanz-Alonso; Jesús María Sanz-Serna (J. Comput. Phys., 2021)

Hierarchical Ensemble Kalman Methods with Sparsity-Promoting Generalized Gamma Hyperpriors. - Hwanwoo Kim; Daniel Sanz-Alonso; Alexander Strang (CoRR, 2022)

Mathematical Foundations of Graph-Based Bayesian Semi-Supervised Learning. - Nicolás García Trillos; Daniel Sanz-Alonso; Ruiyi Yang (CoRR, 2022)

Non-Asymptotic Analysis of Ensemble Kalman Updates: Effective Dimension and Localization. - Omar Al Ghattas; Daniel Sanz-Alonso (CoRR, 2022)

Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective. - Daniel Sanz-Alonso; Ruiyi Yang (J. Mach. Learn. Res., 2022)

A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors. - Shiv Agrawal; Hwanwoo Kim; Daniel Sanz-Alonso; Alexander Strang (SIAM/ASA J. Uncertain. Quantification, 2022)

Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy. - Daniel Sanz-Alonso; Ruiyi Yang (SIAM/ASA J. Uncertain. Quantification, 2022)

Autodifferentiable Ensemble Kalman Filters. - Yuming Chen; Daniel Sanz-Alonso; Rebecca Willett (SIAM J. Math. Data Sci., 2022)

Reduced-Order Autodifferentiable Ensemble Kalman Filters. - Yuming Chen; Daniel Sanz-Alonso; Rebecca Willett (CoRR, 2023)

Bayesian Optimization with Noise-Free Observations: Improved Regret Bounds via Random Exploration. - Hwanwoo Kim; Daniel Sanz-Alonso (CoRR, 2024)

Data Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet. - Melissa Adrian; Daniel Sanz-Alonso; Rebecca Willett (CoRR, 2024)

A First Course in Monte Carlo Methods. - Daniel Sanz-Alonso; Omar Al Ghattas (CoRR, 2024)

Inverse Problems and Data Assimilation: A Machine Learning Approach. - Eviatar Bach; Ricardo Baptista; Daniel Sanz-Alonso; Andrew M. Stuart (CoRR, 2024)

Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems. - Daniel Sanz-Alonso; Nathan Waniorek (CoRR, 2024)

Ensemble Kalman Filters with Resampling. - Omar Al Ghattas; Jiajun Bao; Daniel Sanz-Alonso (SIAM/ASA J. Uncertain. Quantification, 2024)

Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement. - Daniel Sanz-Alonso; Nathan Waniorek (SIAM/ASA J. Uncertain. Quantification, 2024)

Optimization on Manifolds via Graph Gaussian Processes. - Hwanwoo Kim; Daniel Sanz-Alonso; Ruiyi Yang (SIAM J. Math. Data Sci., 2024)

Precision and Cholesky Factor Estimation for Gaussian Processes. - Jiaheng Chen; Daniel Sanz-Alonso (SIAM/ASA J. Uncertain. Quantification, 2025)

Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration. - Hwanwoo Kim; Daniel Sanz-Alonso (SIAM/ASA J. Uncertain. Quantification, 2025)

Gaussian Process Regression under Computational and Epistemic Misspecification. - Daniel Sanz-Alonso; Ruiyi Yang (SIAM J. Numer. Anal., 2025)

Hierarchical Bayesian Inverse Problems: A High-Dimensional Statistics Viewpoint. - Daniel Sanz-Alonso; Nathan Waniorek (SIAM Rev., 2025)

Functional Multi-Reference Alignment via Deconvolution. - Omar Al Ghattas; Anna Little; Daniel Sanz-Alonso; Mikhail Sweeney (CoRR, 2025)