VBFF Publications
2025
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]
K. Bhattacharya, N. Kovachki, A. Rajan, A. M. Stuart, M. Trautner; Learning Homogenization for Elliptic Operators, SIAM Journal on Numerical Analysis, Volume 62, Issue 4, Aug 2024, Pages 1465-2070.
[Online Publication]
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
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]
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]
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]
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
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]
