D. Sanz-Alonso

发表

Importance Sampling: Intrinsic Dimension and Computational Cost

O. Papaspiliopoulos, A. Stuart, S. Agapiou, 2015, 1511.06196.

HMC: avoiding rejections by not using leapfrog and an analysis of the acceptance rate

J. M. Sanz-Serna, M. Calvo, D. Sanz-Alonso, 2019 .

Posterior Contraction Rates for Graph-Based Semi-Supervised Classification

D. Sanz-Alonso, Ruiyi Yang, 2020, 2008.11809.

The Bayesian Update: Variational Formulations and Gradient Flows

Nicolas Garcia Trillos, Nicolás García Trillos, Daniel Sanz-Alonso, 2017 .

Importance Sampling: Computational Complexity and Intrinsic Dimension

Andrew M. Stuart, Omiros Papaspiliopoulos, Daniel Sanz-Alonso, 2015 .

Continuum Limits of Posteriors in Graph Bayesian Inverse Problems

Nicolás García Trillos, Daniel Sanz-Alonso, D. Sanz-Alonso, 2017, SIAM J. Math. Anal..

Hierarchical Ensemble Kalman Methods with Sparsity-Promoting Generalized Gamma Hyperpriors

D. Sanz-Alonso, A. Strang, Hwanwoo Kim, 2022, Foundations of Data Science.

Optimization on Manifolds via Graph Gaussian Processes

D. Sanz-Alonso, Ruiyi Yang, Hwanwoo Kim, 2022, ArXiv.

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

D. Sanz-Alonso, Omar Al Ghattas, 2022, ArXiv.

Mathematical Foundations of Graph-Based Bayesian Semi-Supervised Learning

D. Sanz-Alonso, Ruiyi Yang, N. G. Trillos, 2022, Notices of the American Mathematical Society.

Graph-based prior and forward models for inverse problems on manifolds with boundaries pdf

Shixiao W. Jiang, John Harlim, Daniel Sanz-Alonso, 2021, Inverse Problems.

Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds

John Harlim, Daniel Sanz-Alonso, Ruiyi Yang, 2019, SIAM/ASA J. Uncertain. Quantification.

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

Nicolás García Trillos, Daniel Sanz-Alonso, Ruiyi Yang, 2019, J. Mach. Learn. Res..

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

Zachary T. Kaplan, Nicolás García Trillos, Daniel Sanz-Alonso, 2020, J. Mach. Learn. Res..

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

Zachary T. Kaplan, Nicolás García Trillos, Daniel Sanz-Alonso, 2017, ArXiv.

A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors

Daniel Sanz-Alonso, Shiv Agrawal, Hwanwoo Kim, 2021, SIAM/ASA J. Uncertain. Quantification.

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

Jesús María Sanz-Serna, J. M. Sanz-Serna, Daniel Sanz-Alonso, 2021, J. Comput. Phys..

Inverse Problems and Data Assimilation

A. Stuart, D. Sanz-Alonso, A. Taeb, 2018, 1810.06191.

Reduced-order autodifferentiable ensemble Kalman filters

R. Willett, D. Sanz-Alonso, Yuming Chen, 2023, Inverse Problems.

From Optimization to Sampling Through Gradient Flows

D. Sanz-Alonso, B. Hosseini, N. G. Trillos, 2023, Notices of the American Mathematical Society.

The Bayesian formulation and well-posedness of fractional elliptic inverse problems

Nicolas Garcia Trillos, Daniel Sanz-Alonso, D. Sanz-Alonso, 2016, 1611.05475.

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

J. M. Sanz-Serna, M. Calvo, D. Sanz-Alonso, 2019, ArXiv.

The SPDE Approach to Matérn Fields: Graph Representations pdf

Daniel Sanz-Alonso, Ruiyi Yang, D. Sanz-Alonso, 2020, Statistical Science.

Optimization on Manifolds via Graph Gaussian Processes

D. Sanz-Alonso, Ruiyi Yang, Hwanwoo Kim, 2022, ArXiv.

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

D. Sanz-Alonso, Ruiyi Yang, 2021, SIAM/ASA J. Uncertain. Quantification.

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

D. Sanz-Alonso, Ruiyi Yang, 2020, J. Mach. Learn. Res..

Filter accuracy for the Lorenz 96 model: Fixed versus adaptive observation operators

Kody J. H. Law, Andrew M. Stuart, Daniel Sanz-Alonso, 2016 .

Importance Sampling and Necessary Sample Size: An Information Theory Approach

Daniel Sanz-Alonso, D. Sanz-Alonso, 2016, SIAM/ASA J. Uncertain. Quantification.

Data-driven forward discretizations for Bayesian inversion

Y. Marzouk, D. Sanz-Alonso, D. Bigoni, 2020, Inverse Problems.

Auto-differentiable Ensemble Kalman Filters pdf

Rebecca Willett, Daniel Sanz-Alonso, Yuming Chen, 2021, SIAM J. Math. Data Sci..

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

Andrew M. Stuart, Daniel Sanz-Alonso, A. Stuart, 2014, SIAM/ASA J. Uncertain. Quantification.

Spatial extreme values: variational techniques and stochastic integrals

D. Sanz-Alonso, Ryan W. Murray, N. G. Trillos, 2018, 1808.03218.

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

D. Sanz-Alonso, Nathan Waniorek, 2023, SIAM/ASA J. Uncertain. Quantification.

Covariance Operator Estimation: Sparsity, Lengthscale, and Ensemble Kalman Filters

D. Sanz-Alonso, Nathan Waniorek, Omar Al-Ghattas, 2023, 2310.16933.

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

D. Sanz-Alonso, Omar Al Ghattas, 2022, Information and Inference: A Journal of the IMA.

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

Neil K. Chada, Yuming Chen, Daniel Sanz-Alonso, 2020, Foundations of Data Science.

Gaussian Process Regression under Computational and Epistemic Misspecification

D. Sanz-Alonso, Ruiyi Yang, 2023, 2312.09225.

The Bayesian Update: Variational Formulations and Gradient Flows

D. Sanz-Alonso, N. G. Trillos, 2017, Bayesian Analysis.