VBFF Publications

2025

[203] O. D. Akyildiz, M. Girolami, A. M. Stuart, A. Vadeboncoeur; Efficient Prior Calibration From Indirect Data, SIAM Journal on Scientific Computing, Vol. 47, No. 4, pp C932-C958, 2025.
[Online Publication] [pdf]

[27c] E. Calvello, S. Reich, A. M. Stuart; Ensemble Kalman Methods: A Mean Field Perspective, Acta Numerica (2025), pp. 123–291.
[Publisher] [pdf]

[26c] T. Helin, A.M. Stuart, A.L. Teckentrup, K.C. Zygalakis. Introduction To Gaussian Process Regression In Bayesian Inverse Problems, With New Results On Experimental Design For Weighted Error Measures. In: A. Hinrichs, P. Kritzer, F. Pillichshammer (eds.). Monte Carlo and Quasi-Monte Carlo Methods 2022, (pp. 49-79). Springer, 2024.
[Publisher] [pdf]

[25c] N. B. Kovachki, S. Lanthaler, A. M. Stuart; Operator Learning: Algorithms and Analysis;
Handbook of Numerical Analysis, Numerical Analysis Meets Machine Learning
Edited by Siddhartha Mishra, Alex Townsend, Elsevier 2024.
[Publisher] [pdf]

[202] Y. Chen, B. Hosseini, H. Owhadi, A. M. Stuart; Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulation, Statistics and Computing 35 (1), 1-23, 2025.
[Online Publication] [pdf]

[201] Ziming Liu, Andrew Stuart, Yixuan Wang; Second order ensemble Langevin method for sampling and inverse problems, Communications in Mathematical Sciences, Vol. 23, No. 5, pp. 1299–1317, 2025.
[Online Publication] [pdf]

[200] P. Batlle, Y. Chen, B. Hosseini, H. Owhadi, A. M. Stuart; Error analysis of kernel/GP methods for nonlinear and parametric PDEs, Journal of Computational Physics 520, 113488, 2025
[Online Publication] [pdf]

[199] H. Kaveh, J. Philippe Avouac, A. M. Stuart; Spatiotemporal forecast of extreme events in a chaotic model of slow slip events, Geophysical Journal International 240 (2), 870-885, 2025
[Online Publication] [pdf]

[198] J. Parres-Gold, M. Levine, B. Emert, A. Stuart, M.l B. Elowitz; Contextual computation by competitive protein dimerization networks; Cell 188, 1–19 April 3, 2025
[Read]

O. Dunbar, N. Nelsen, M. Mutic; Hyperparameter Optimization for Randomized Algorithms: A Case Study for Random Features, Statistics and Computing, Vol. 35, No. 56, 2025.
[Online Publication] [pdf]

Z. Wang, R. Baptista, Y. Marzouk, L. Ruthotto, D. Verma; Efficient neural network approaches for conditional optimal transport with applications in Bayesian inference, SIAM Journal on Scientific Computing, In Press.
[Online Publication] [pdf]

B. Feng, R. Baptista, K. L. Bouman; Neural Approximate Mirror Maps for Constrained Diffusion Models, The Thirteenth International Conference on Learning Representations, 2025.
[Online Publication] [pdf]

J. Newey, J. P. Whitehead, E. Carlson; Model discovery on the fly using continuous data assimilation, Journal of Computational Physics Volume 537, 15 September 2025, 114121.
[Online Publication] [pdf]

S. Liaw, R. Morrison, Y. Marzouk, R. Baptista; Learning Local Neighborhoods of Non-Gaussian Graphical Models, Proceedings of the AAAI Conference on Artificial Intelligence 39 (18), 2025.
[Online Publication] [pdf]

A. Wang, H. Zheng, Z. Wu, R. Baptista, D. Z. Huang, Y. Yue; Ensemble Kalman sampling and diffusion prior in tandem: A split Gibbs framework, Frontiers in Probabilistic Inference: Learning meets Sampling Workshop at International Conference of Learning Representations, 2025.
[pdf]

R. Baptista, A. A. Pooladian, M. Brennan, Y. Marzouk, J. Niles-Weed; Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps, The 28th International Conference on Artificial Intelligence and Statistics, 2025.
[Online Publication]

R. Baptista, M. Brennan, Y. Marzouk, Dimension reduction via score ratio matching, Transactions of Machine Learning, 2025.
[Online Publication] [pdf]

H. Zheng, W. Chu, A. Wang, N. Kovachki, R. Baptista, Y. Yue; Ensemble Kalman Diffusion Guidance: A Derivative-free Method for Inverse Problems, Transactions of Machine Learning, 2025.
[Online Publication] [pdf]

Q. Chen, E. Arnaud, R. Baptista, O. Zahm, Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis, SIAM Journal on Scientific Computing, In Press.
[Online Publication] [pdf]

2024

[197] Y. Chen, D. Z. Huang, J. Huang, S. Reich, A. M. Stuart; Efficient, multimodal, and derivative-free bayesian inference with Fisher–Rao gradient flows, Inverse Problems 40 (2024) 125001 (39pp)
[Online Publication] [pdf]

[196] J. A. Carrillo, F. Hoffmann, A. M. Stuart, and U. Vaes; The Mean-Field Ensemble Kalman Filter: Near-Gaussian Setting, SIAM Journal on Numerical Analysis, Vol. 62, Iss. 6 (2024)
[Online Publication] [pdf]

[194] K. Bhattacharya, N. B. Kovachki, A. Rajan, A. M. Stuart, M. Trautner; Learning Homogenization for Elliptic Operators, SIAM Journal of Numerical Analysis, Vol. 62, Issue 4 (2024)
[Online Publication] [pdf]

A. Satish, R. Baptista, F. Hoffmann; Consensus Based Optimization Accelerates Gradient Descent, 16th Annual Workshop on Optimization for Machine Learning at Neural Information Processing Systems, 2024.
[pdf]

T. Bourdais, P. Batlle, X. Yang, R. Baptista, N. Rouquette, H. Owhadi; Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots, Proceedings of the National Academy of Sciences 121 (32), e2403449121, 2024.
[Online Publication] [pdf]

D. Vishny, M. Morzfeld, K. Gwirtz, E. Bach, O. R. A. Dunbar, D. Hodyss; High-Dimensional Covariance Estimation From a Small Number of Samples, Journal of Advances in Modeling Earth Systems, Volume 16, Issue 9, 2024.
[Online Publication] [pdf]

[193] N. H. Nelsen, A. M. Stuart; Operator Learning Using Random Features: A Tool for Scientific Computing; SIAM Review, Vol. 66, No. 3, pp. 535–571, 2024.
[Online Publication] [pdf]

[192] J.-L. Wu, M. E. Levine, T. Schneider, A. M. Stuart; Learning about structural errors in models of complex dynamical systems; Journal of Computational Physics, 513 (2024) 113157.
[Online Publication] [pdf]

[191] E. Bach, T. Colonius, I. Scherl, A. M. Stuart; Filtering Dynamical Systems Using Observations of Statistics; Chaos, Vol. 34, Issue 3, March 2024.
[Online Publication] [pdf]

D. Huang, N. H. Nelsen, M. Trautner; An operator learning perspective on parameter-to-observable maps, Foundations of Data Science, 2024.
[Online Publication] [pdf]

Z. Wan, R. Baptista, Y. Chen, J. Anderson, A. Boral, F. Sha, L. Zepeda-Núñez; Debias coarsely, sample conditionally: Statistical downscaling through optimal transport and probabilistic diffusion models, Advances in Neural Information Processing Systems, Vol. 36, 2024.
[Online Publication] [pdf]

M. Alain, S. Takao, B. Paige, M. Deisenroth; Gaussian Processes on Cellular Complexes, International Conference on Machine Learning, 2024.
[Online Publication] [pdf]

R. Anderka, M. Deisenroth, S. Takao; Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems, Uncertainty in Artificial Intelligence, 2024.
[Online Publication] [pdf]

C. Au, M. Tsamados, P. Manescu, S. Takao; ARISGAN: Extreme Super-Resolution of Arctic Surface Imagery using Generative Adversarial Networks, Frontiers in Remote Sensing, 2024.
[Online Publication] [pdf]

W. Chen, M. Tsamados, R. Willatt, D. Brockley, M. Deisenroth, C. de Rijke-Thomas, A. Francis, L. Hirata, T. Johnson, I. Lawrence, S. Takao and others; Co-located OLCI optical imagery and SAR altimetry from Sentinel-3 for enhanced Arctic spring sea ice surface classification, Frontiers in Remote Sensing, 2024.
[Online Publication] [pdf]

W. Gregory, R. MacEachern, S. Takao, I. Lawrence, C. Nab, M. P. Deisenroth, M. Tsamados; Scalable interpolation of satellite altimetry data with probabilistic machine learning, Nature Communications 15, Article number: 7453 (2024).
[Online Publication] [pdf]

2023

[188] B. Liu, E. Ocegueda, M. Trautner, A. M. Stuart, K. Bhattacharya; Learning Macroscopic Internal Variables and History Dependence From Microscopic Models, J. Mech. Phys. Solids, Vol. 178 (2023) 105329.
[Online Publication] [pdf]

J. Alfonso, R. Baptista, A. Bhakta, N. Gal, A. Hou, I.Lyubimova, D. Pocklington, J. Sajonz, G. Trigila, R. Tsai; A generative flow for conditional sampling via optimal transport, NeurIPS Optimal Transport Workshop, 2023.
[Online Publication] [pdf]

S. Lanthaler, N. Nelsen; Advances; Error bounds for learning with vector-valued random features, Neural Information Processing Systems, Curran Associates, Inc., 71834--71861, Vol. 36, 2023.
[Online Publication] [pdf]