Daniel Sanz-Alonso

50 papers Journal 50

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Auto-differentiable data assimilation: Co-learning of states, dynamics, and filtering algorithms. CoRR Melissa Adrian, Daniel Sanz-Alonso, Rebecca Willett
2026	J	`jnl`	Convergence Rates for Learning Pseudo-Differential Operators. CoRR Jiaheng Chen, Daniel Sanz-Alonso
2025	J	`jnl`	Bayesian Optimization on Networks. CoRR Wenwen Li, Daniel Sanz-Alonso, Ruiyi Yang
2025	J	`jnl`	Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration. SIAM/ASA J. Uncertain. Quantification Hwanwoo Kim, Daniel Sanz-Alonso
2025	J	`jnl`	Functional Multi-Reference Alignment via Deconvolution. CoRR Omar Al Ghattas, Anna Little, Daniel Sanz-Alonso, Mikhail Sweeney
2025	J	`jnl`	Gaussian Process Regression under Computational and Epistemic Misspecification. SIAM J. Numer. Anal. Daniel Sanz-Alonso, Ruiyi Yang
2025	J	`jnl`	Hierarchical Bayesian Inverse Problems: A High-Dimensional Statistics Viewpoint. SIAM Rev. Daniel Sanz-Alonso, Nathan Waniorek
2025	J	`jnl`	Precision and Cholesky Factor Estimation for Gaussian Processes. SIAM/ASA J. Uncertain. Quantification Jiaheng Chen, Daniel Sanz-Alonso
2024	J	`jnl`	A First Course in Monte Carlo Methods. CoRR Daniel Sanz-Alonso, Omar Al Ghattas
2024	J	`jnl`	Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement. SIAM/ASA J. Uncertain. Quantification Daniel Sanz-Alonso, Nathan Waniorek
2024	J	`jnl`	Bayesian Optimization with Noise-Free Observations: Improved Regret Bounds via Random Exploration. CoRR Hwanwoo Kim, Daniel Sanz-Alonso
2024	J	`jnl`	Data Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet. CoRR Melissa Adrian, Daniel Sanz-Alonso, Rebecca Willett
2024	J	`jnl`	Ensemble Kalman Filters with Resampling. SIAM/ASA J. Uncertain. Quantification Omar Al Ghattas, Jiajun Bao, Daniel Sanz-Alonso
2024	J	`jnl`	Hierarchical Bayesian Inverse Problems: A High-Dimensional Statistics Viewpoint. CoRR Daniel Sanz-Alonso, Nathan Waniorek
2024	J	`jnl`	Inverse Problems and Data Assimilation: A Machine Learning Approach. CoRR Eviatar Bach, Ricardo Baptista, Daniel Sanz-Alonso, Andrew M. Stuart
2024	J	`jnl`	Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems. CoRR Daniel Sanz-Alonso, Nathan Waniorek
2024	J	`jnl`	Optimization on Manifolds via Graph Gaussian Processes. SIAM J. Math. Data Sci. Hwanwoo Kim, Daniel Sanz-Alonso, Ruiyi Yang
2024	J	`jnl`	Precision and Cholesky Factor Estimation for Gaussian Processes. CoRR Jiaheng Chen, Daniel Sanz-Alonso
2023	J	`jnl`	Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators Under Mesh Refinement. CoRR Daniel Sanz-Alonso, Nathan Waniorek
2023	J	`jnl`	Ensemble Kalman Filters with Resampling. CoRR Omar Al Ghattas, Jiajun Bao, Daniel Sanz-Alonso
2023	J	`jnl`	Gaussian Process Regression under Computational and Epistemic Misspecification. CoRR Daniel Sanz-Alonso, Ruiyi Yang
2023	J	`jnl`	Reduced-Order Autodifferentiable Ensemble Kalman Filters. CoRR Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett
2022	J	`jnl`	A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors. SIAM/ASA J. Uncertain. Quantification Shiv Agrawal, Hwanwoo Kim, Daniel Sanz-Alonso, Alexander Strang
2022	J	`jnl`	Autodifferentiable Ensemble Kalman Filters. SIAM J. Math. Data Sci. Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett
2022	J	`jnl`	Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy. SIAM/ASA J. Uncertain. Quantification Daniel Sanz-Alonso, Ruiyi Yang
2022	J	`jnl`	Hierarchical Ensemble Kalman Methods with Sparsity-Promoting Generalized Gamma Hyperpriors. CoRR Hwanwoo Kim, Daniel Sanz-Alonso, Alexander Strang
2022	J	`jnl`	Mathematical Foundations of Graph-Based Bayesian Semi-Supervised Learning. CoRR Nicolás García Trillos, Daniel Sanz-Alonso, Ruiyi Yang
2022	J	`jnl`	Non-Asymptotic Analysis of Ensemble Kalman Updates: Effective Dimension and Localization. CoRR Omar Al Ghattas, Daniel Sanz-Alonso
2022	J	`jnl`	Optimization on Manifolds via Graph Gaussian Processes. CoRR Hwanwoo Kim, Daniel Sanz-Alonso, Ruiyi Yang
2022	J	`jnl`	Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective. J. Mach. Learn. Res. Daniel Sanz-Alonso, Ruiyi Yang
2021	J	`jnl`	Auto-differentiable Ensemble Kalman Filters. CoRR Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett
2021	J	`jnl`	Bayesian Update with Importance Sampling: Required Sample Size. Entropy Daniel Sanz-Alonso, Zijian Wang
2021	J	`jnl`	Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy. CoRR Daniel Sanz-Alonso, Ruiyi Yang
2021	J	`jnl`	Graph-based Prior and Forward Models for Inverse Problems on Manifolds with Boundaries. CoRR John Harlim, Shixiao W. Jiang, Hwanwoo Kim, Daniel Sanz-Alonso
2021	J	`jnl`	HMC: Reducing the number of rejections by not using leapfrog and some results on the acceptance rate. J. Comput. Phys. Mari Paz Calvo, Daniel Sanz-Alonso, Jesús María Sanz-Serna
2020	J	`jnl`	Data-Driven Forward Discretizations for Bayesian Inversion. CoRR Daniele Bigoni, Yuming Chen, Nicolás García Trillos, Youssef M. Marzouk, Daniel Sanz-Alonso
2020	J	`jnl`	Iterative Ensemble Kalman Methods: A Unified Perspective with Some New Variants. CoRR Neil K. Chada, Yuming Chen, Daniel Sanz-Alonso
2020	J	`jnl`	Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds. SIAM/ASA J. Uncertain. Quantification John Harlim, Daniel Sanz-Alonso, Ruiyi Yang
2020	J	`jnl`	On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms. J. Mach. Learn. Res. Nicolás García Trillos, Zachary Kaplan, Thabo Samakhoana, Daniel Sanz-Alonso
2020	J	`jnl`	The SPDE Approach to Matérn Fields: Graph Representations. CoRR Daniel Sanz-Alonso, Ruiyi Yang
2019	J	`jnl`	HMC: avoiding rejections by not using leapfrog and some results on the acceptance rate. CoRR Mari Paz Calvo, Daniel Sanz-Alonso, Jesús María Sanz-Serna
2019	J	`jnl`	Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds. CoRR John Harlim, Daniel Sanz-Alonso, Ruiyi Yang
2019	J	`jnl`	Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis. CoRR Nicolás García Trillos, Daniel Sanz-Alonso, Ruiyi Yang
2019	J	`jnl`	Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis. J. Mach. Learn. Res. Nicolás García Trillos, Daniel Sanz-Alonso, Ruiyi Yang
2019	J	`jnl`	Variational Characterizations of Local Entropy and Heat Regularization in Deep Learning. CoRR Nicolás García Trillos, Zachary Kaplan, Daniel Sanz-Alonso
2019	J	`jnl`	Variational Characterizations of Local Entropy and Heat Regularization in Deep Learning. Entropy Nicolás García Trillos, Zachary Kaplan, Daniel Sanz-Alonso
2018	J	`jnl`	Continuum Limits of Posteriors in Graph Bayesian Inverse Problems. SIAM J. Math. Anal. Nicolás García Trillos, Daniel Sanz-Alonso
2018	J	`jnl`	Importance Sampling and Necessary Sample Size: An Information Theory Approach. SIAM/ASA J. Uncertain. Quantification Daniel Sanz-Alonso
2017	J	`jnl`	On the Consistency of Graph-based Bayesian Learning and the Scalability of Sampling Algorithms. CoRR Nicolás García Trillos, Zachary Kaplan, Thabo Samakhoana, Daniel Sanz-Alonso
2015	J	`jnl`	Long-Time Asymptotics of the Filtering Distribution for Partially Observed Chaotic Dynamical Systems. SIAM/ASA J. Uncertain. Quantification Daniel Sanz-Alonso, Andrew M. Stuart