Bamdad Hosseini

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 23

Top Topics

Gaussian Process

Metropolis Hastings

Posterior Distribution

Nonlinear Filtering

Top Venues

SIAM/ASA J. Uncertain. Quantification

J. Comput. Phys.

Publications

Pau Batlle, Matthieu Darcy, Bamdad Hosseini, Houman Owhadi
Kernel methods are competitive for operator learning. J. Comput. Phys. 496 (2024)
Biraj Pandey, Bamdad Hosseini, Pau Batlle, Houman Owhadi
Diffeomorphic Measure Matching with Kernels for Generative Modeling. CoRR (2024)
Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M. Stuart
Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulation. CoRR (2024)
Mohammad Al-Jarrah, Niyizhen Jin, Bamdad Hosseini, Amirhossein Taghvaei
Optimal Transport-based Nonlinear Filtering in High-dimensional Settings. CoRR (2023)
Pau Batlle, Matthieu Darcy, Bamdad Hosseini, Houman Owhadi
Kernel Methods are Competitive for Operator Learning. CoRR (2023)
Mohammad Al-Jarrah, Bamdad Hosseini, Amirhossein Taghvaei
Optimal Transport Particle Filters. CDC (2023)
Ricardo Baptista, Bamdad Hosseini, Nikola B. Kovachki, Youssef M. Marzouk, Amir Sagiv
An Approximation Theory Framework for Measure-Transport Sampling Algorithms. CoRR (2023)
Pau Batlle, Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M. Stuart
Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs. CoRR (2023)
Amirhossein Taghvaei, Bamdad Hosseini
An Optimal Transport Formulation of Bayes' Law for Nonlinear Filtering Algorithms. CoRR (2022)
Amirhossein Taghvaei, Bamdad Hosseini
An Optimal Transport Formulation of Bayes' Law for Nonlinear Filtering Algorithms. CDC (2022)
Da Long, Nicole Mrvaljevic, Shandian Zhe, Bamdad Hosseini
A Kernel Approach for PDE Discovery and Operator Learning. CoRR (2022)
Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M. Stuart
Solving and Learning Nonlinear PDEs with Gaussian Processes. CoRR (2021)
Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M. Stuart
Solving and learning nonlinear PDEs with Gaussian processes. J. Comput. Phys. 447 (2021)
Nicolás García Trillos, Franca Hoffmann, Bamdad Hosseini
Geometric structure of graph Laplacian embeddings. J. Mach. Learn. Res. 22 (2021)
Kaushik Bhattacharya, Bamdad Hosseini, Nikola B. Kovachki, Andrew M. Stuart
Model Reduction and Neural Networks for Parametric PDEs. CoRR (2020)
Nikola B. Kovachki, Ricardo Baptista, Bamdad Hosseini, Youssef M. Marzouk
Conditional Sampling With Monotone GANs. CoRR (2020)
Franca Hoffmann, Bamdad Hosseini, Zhi Ren, Andrew M. Stuart
Consistency of Semi-Supervised Learning Algorithms on Graphs: Probit and One-Hot Methods. J. Mach. Learn. Res. 21 (2020)
Andrea L. Bertozzi, Bamdad Hosseini, Hao Li, Kevin Miller, Andrew M. Stuart
Posterior Consistency of Semi-Supervised Regression on Graphs. CoRR (2020)
Franca Hoffmann, Bamdad Hosseini, Zhi Ren, Andrew M. Stuart
Consistency of semi-supervised learning algorithms on graphs: Probit and one-hot methods. CoRR (2019)
Bamdad Hosseini
Two Metropolis-Hastings Algorithms for Posterior Measures with Non-Gaussian Priors in Infinite Dimensions. SIAM/ASA J. Uncertain. Quantification 7 (4) (2019)
Bamdad Hosseini, Nilima Nigam
Well-Posed Bayesian Inverse Problems: Priors with Exponential Tails. SIAM/ASA J. Uncertain. Quantification 5 (1) (2017)
Bamdad Hosseini
Well-Posed Bayesian Inverse Problems with Infinitely Divisible and Heavy-Tailed Prior Measures. SIAM/ASA J. Uncertain. Quantification 5 (1) (2017)
Bamdad Hosseini, Nilima Nigam, John M. Stockie
On regularizations of the Dirac delta distribution. J. Comput. Phys. 305 (2016)