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Daniel Sanz-Alonso
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2020 – today
- 2024
- [j16]Daniel Sanz-Alonso, Nathan Waniorek:
Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement. SIAM/ASA J. Uncertain. Quantification 12(1): 30-68 (2024) - [j15]Omar Al Ghattas, Jiajun Bao, Daniel Sanz-Alonso:
Ensemble Kalman Filters with Resampling. SIAM/ASA J. Uncertain. Quantification 12(2): 411-441 (2024) - [j14]Hwanwoo Kim, Daniel Sanz-Alonso, Ruiyi Yang:
Optimization on Manifolds via Graph Gaussian Processes. SIAM J. Math. Data Sci. 6(1): 1-25 (2024) - [i24]Daniel Sanz-Alonso, Nathan Waniorek:
Hierarchical Bayesian Inverse Problems: A High-Dimensional Statistics Viewpoint. CoRR abs/2401.03074 (2024) - [i23]Hwanwoo Kim, Daniel Sanz-Alonso:
Bayesian Optimization with Noise-Free Observations: Improved Regret Bounds via Random Exploration. CoRR abs/2401.17037 (2024) - [i22]Melissa Adrian, Daniel Sanz-Alonso, Rebecca Willett:
Data Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet. CoRR abs/2405.13180 (2024) - [i21]Daniel Sanz-Alonso, Omar Al Ghattas:
A First Course in Monte Carlo Methods. CoRR abs/2405.16359 (2024) - [i20]Eviatar Bach, Ricardo Baptista, Daniel Sanz-Alonso, Andrew Stuart:
Inverse Problems and Data Assimilation: A Machine Learning Approach. CoRR abs/2410.10523 (2024) - 2023
- [i19]Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett:
Reduced-Order Autodifferentiable Ensemble Kalman Filters. CoRR abs/2301.11961 (2023) - [i18]Daniel Sanz-Alonso, Nathan Waniorek:
Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators Under Mesh Refinement. CoRR abs/2304.09933 (2023) - [i17]Omar Al Ghattas, Jiajun Bao, Daniel Sanz-Alonso:
Ensemble Kalman Filters with Resampling. CoRR abs/2308.08751 (2023) - [i16]Daniel Sanz-Alonso, Ruiyi Yang:
Gaussian Process Regression under Computational and Epistemic Misspecification. CoRR abs/2312.09225 (2023) - 2022
- [j13]Daniel Sanz-Alonso, Ruiyi Yang:
Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective. J. Mach. Learn. Res. 23: 97:1-97:28 (2022) - [j12]Daniel Sanz-Alonso, Ruiyi Yang:
Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy. SIAM/ASA J. Uncertain. Quantification 10(4): 1323-1349 (2022) - [j11]Shiv Agrawal, Hwanwoo Kim, Daniel Sanz-Alonso, Alexander Strang:
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors. SIAM/ASA J. Uncertain. Quantification 10(4): 1533-1559 (2022) - [j10]Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett:
Autodifferentiable Ensemble Kalman Filters. SIAM J. Math. Data Sci. 4(2): 801-833 (2022) - [i15]Hwanwoo Kim, Daniel Sanz-Alonso, Alexander Strang:
Hierarchical Ensemble Kalman Methods with Sparsity-Promoting Generalized Gamma Hyperpriors. CoRR abs/2205.09322 (2022) - [i14]Nicolás García Trillos, Daniel Sanz-Alonso, Ruiyi Yang:
Mathematical Foundations of Graph-Based Bayesian Semi-Supervised Learning. CoRR abs/2207.01093 (2022) - [i13]Omar Al Ghattas, Daniel Sanz-Alonso:
Non-Asymptotic Analysis of Ensemble Kalman Updates: Effective Dimension and Localization. CoRR abs/2208.03246 (2022) - [i12]Hwanwoo Kim, Daniel Sanz-Alonso, Ruiyi Yang:
Optimization on Manifolds via Graph Gaussian Processes. CoRR abs/2210.10962 (2022) - 2021
- [j9]Daniel Sanz-Alonso, Zijian Wang:
Bayesian Update with Importance Sampling: Required Sample Size. Entropy 23(1): 22 (2021) - [j8]Mari Paz Calvo, Daniel Sanz-Alonso, Jesús María Sanz-Serna:
HMC: Reducing the number of rejections by not using leapfrog and some results on the acceptance rate. J. Comput. Phys. 437: 110333 (2021) - [i11]John Harlim, Shixiao W. Jiang, Hwanwoo Kim, Daniel Sanz-Alonso:
Graph-based Prior and Forward Models for Inverse Problems on Manifolds with Boundaries. CoRR abs/2106.06787 (2021) - [i10]Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett:
Auto-differentiable Ensemble Kalman Filters. CoRR abs/2107.07687 (2021) - [i9]Daniel Sanz-Alonso, Ruiyi Yang:
Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy. CoRR abs/2109.02777 (2021) - 2020
- [j7]Nicolás García Trillos, Zachary Kaplan, Thabo Samakhoana, Daniel Sanz-Alonso:
On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms. J. Mach. Learn. Res. 21: 28:1-28:47 (2020) - [j6]John Harlim, Daniel Sanz-Alonso, Ruiyi Yang:
Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds. SIAM/ASA J. Uncertain. Quantification 8(4): 1414-1445 (2020) - [i8]Daniele Bigoni, Yuming Chen, Nicolás García Trillos, Youssef M. Marzouk, Daniel Sanz-Alonso:
Data-Driven Forward Discretizations for Bayesian Inversion. CoRR abs/2003.07991 (2020) - [i7]Daniel Sanz-Alonso, Ruiyi Yang:
The SPDE Approach to Matérn Fields: Graph Representations. CoRR abs/2004.08000 (2020) - [i6]Neil K. Chada, Yuming Chen, Daniel Sanz-Alonso:
Iterative Ensemble Kalman Methods: A Unified Perspective with Some New Variants. CoRR abs/2010.13299 (2020)
2010 – 2019
- 2019
- [j5]Nicolás García Trillos, Zachary Kaplan, Daniel Sanz-Alonso:
Variational Characterizations of Local Entropy and Heat Regularization in Deep Learning. Entropy 21(5): 511 (2019) - [j4]Nicolás García Trillos, Daniel Sanz-Alonso, Ruiyi Yang:
Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis. J. Mach. Learn. Res. 20: 136:1-136:37 (2019) - [i5]Nicolás García Trillos, Zachary Kaplan, Daniel Sanz-Alonso:
Variational Characterizations of Local Entropy and Heat Regularization in Deep Learning. CoRR abs/1901.10082 (2019) - [i4]Nicolás García Trillos, Daniel Sanz-Alonso, Ruiyi Yang:
Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis. CoRR abs/1904.03335 (2019) - [i3]John Harlim, Daniel Sanz-Alonso, Ruiyi Yang:
Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds. CoRR abs/1910.10669 (2019) - [i2]Mari Paz Calvo, Daniel Sanz-Alonso, Jesús María Sanz-Serna:
HMC: avoiding rejections by not using leapfrog and some results on the acceptance rate. CoRR abs/1912.03253 (2019) - 2018
- [j3]Daniel Sanz-Alonso:
Importance Sampling and Necessary Sample Size: An Information Theory Approach. SIAM/ASA J. Uncertain. Quantification 6(2): 867-879 (2018) - [j2]Nicolás García Trillos, Daniel Sanz-Alonso:
Continuum Limits of Posteriors in Graph Bayesian Inverse Problems. SIAM J. Math. Anal. 50(4): 4020-4040 (2018) - 2017
- [i1]Nicolás García Trillos, Zachary Kaplan, Thabo Samakhoana, Daniel Sanz-Alonso:
On the Consistency of Graph-based Bayesian Learning and the Scalability of Sampling Algorithms. CoRR abs/1710.07702 (2017) - 2015
- [j1]Daniel Sanz-Alonso, Andrew M. Stuart:
Long-Time Asymptotics of the Filtering Distribution for Partially Observed Chaotic Dynamical Systems. SIAM/ASA J. Uncertain. Quantification 3(1): 1200-1220 (2015)
Coauthor Index
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