32 citations · 183 across the 54 of their papers we have counts for
6 papers · 1 filter
Gaussian Process Regression under Computational and Epistemic Misspecification
Daniel Sanz-Alonso, Ruiyi Yang
Gaussian process regression is a classical kernel method for function estimation and data interpolation. In large data applications, computational costs can be reduced using low-ra…
Covariance Operator Estimation: Sparsity, Lengthscale, and Ensemble Kalman Filters
Omar Al-Ghattas, Jiaheng Chen, Daniel Sanz-Alonso +1
This paper investigates covariance operator estimation via thresholding. For Gaussian random fields with approximately sparse covariance operators, we establish non-asymptotic boun…
Ensemble Kalman Filters with Resampling
Omar Al Ghattas, Jiajun Bao, Daniel Sanz-Alonso
Filtering is concerned with online estimation of the state of a dynamical system from partial and noisy observations. In applications where the state of the system is high dimensio…
Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators Under Mesh Refinement
Daniel Sanz-Alonso, Nathan Waniorek
This paper analyzes a popular computational framework to solve infinite-dimensional Bayesian inverse problems, discretizing the prior and the forward model in a finite-dimensional…
From Optimization to Sampling Through Gradient Flows
N. Garcia Trillos, B. Hosseini, D. Sanz-Alonso
This article overviews how gradient flows, and discretizations thereof, are useful to design and analyze optimization and sampling algorithms. The interplay between optimization, s…
Reduced-Order Autodifferentiable Ensemble Kalman Filters
Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett
This paper introduces a computational framework to reconstruct and forecast a partially observed state that evolves according to an unknown or expensive-to-simulate dynamical syste…