R. Baptista

发表

Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport

J. Oden, Y. Marzouk, O. Ghattas, 2022, 2206.11343.

Gradient-based data and parameter dimension reduction for Bayesian models: an information theoretic perspective

Y. Marzouk, O. Zahm, R. Baptista, 2022, 2207.08670.

On the Representation and Learning of Monotone Triangular Transport Maps

Y. Marzouk, O. Zahm, R. Baptista, 2020, Foundations of Computational Mathematics.

Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport

J. Oden, Y. Marzouk, O. Ghattas, 2022, J. Comput. Phys..

A low-rank ensemble Kalman filter for elliptic observations

Y. Marzouk, J. Eldredge, R. Baptista, 2022, Proceedings of the Royal Society A.

Ensemble Transport Smoothing. Part I: Unified Framework

Y. Marzouk, D. McLaughlin, M. Ramgraber, 2022, Journal of Computational Physics: X.

Coupling Techniques for Nonlinear Ensemble Filtering

Ricardo Baptista, Youssef Marzouk, Alessio Spantini, 2019, SIAM Review.

Learning non-Gaussian graphical models via Hessian scores and triangular transport

Rebecca E. Morrison, Y. Marzouk, O. Zahm, 2021, ArXiv.

An adaptive transport framework for joint and conditional density estimation pdf

Ricardo Baptista, Youssef Marzouk, Olivier Zahm, 2020, ArXiv.

Conditional Sampling With Monotone GANs

Nikola B. Kovachki, Y. Marzouk, Bamdad Hosseini, 2020, ArXiv.

Bayesian Optimization of Combinatorial Structures pdf

Matthias Poloczek, Ricardo Baptista, Matthias Poloczek, 2018, ICML.

Bayesian seismic inversion: Measuring Langevin MCMC sample quality with kernels

Youssef M. Marzouk, Lester Mackey, Muhammad Izzatullah, 2020 .

Score-based Diffusion Models in Function Space

Jae Hyun Lim, Vikram S. Voleti, Nikola B. Kovachki, 2023, ArXiv.

Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models

Fei Sha, Yi-Fan Chen, John R. Anderson, 2023, ArXiv.

Conditional Sampling with Monotone GANs: from Generative Models to Likelihood-Free Inference

Nikola B. Kovachki, Y. Marzouk, Bamdad Hosseini, 2020, 2006.06755.

Some greedy algorithms for sparse polynomial chaos expansions

Prasanth B. Nair, Ricardo Baptista, Valentin Stolbunov, 2019, J. Comput. Phys..

Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting

Ricardo Baptista, Youssef Marzouk, Rebecca E. Morrison, 2017, NIPS.

An Approximation Theory Framework for Measure-Transport Sampling Algorithms

Nikola B. Kovachki, Y. Marzouk, Bamdad Hosseini, 2023, ArXiv.

Diagonal nonlinear transformations preserve structure in covariance and precision matrices

Ricardo Baptista, Rebecca E. Morrison, Rebecca E Morrison, 2021, J. Multivar. Anal..

A generative flow for conditional sampling via optimal transport

G. Trigila, Noam Gal, R. Baptista, 2023, ArXiv.

Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference

R. Baptista, Zheyu Oliver Wang, Youssef Marzouk, 2023, arXiv.org.

Ensemble Transport Smoothing. Part II: Nonlinear Updates

Y. Marzouk, D. McLaughlin, M. Ramgraber, 2022, Journal of Computational Physics: X.

Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models

Fei Sha, Yi-Fan Chen, John R. Anderson, 2023, NeurIPS.

MParT: Monotone Parameterization Toolkit

Y. Marzouk, M. Parno, M. Brennan, 2022, Journal of Open Source Software.

Conditional Sampling With Monotone GANs pdf

Bamdad Hosseini, Ricardo Baptista, Youssef Marzouk, 2020, ArXiv.