Neil K. Chada

According to our database, Neil K. Chada authored at least 24 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics.

Daniel Paulin, Peter A. Whalley, Neil K. Chada, Benedict J. Leimkuhler

CoRR, 2024

A Stochastic Iteratively Regularized Gauss-Newton Method.

El Houcine Bergou, Neil K. Chada, Youssef Diouane

CoRR, 2024

The Ensemble Kalman Filter for Dynamic Inverse Problems.

Simon Weissmann, Neil K. Chada, Xin T. Tong

CoRR, 2024

2023

Unbiased Estimation Using Underdamped Langevin Dynamics.

Hamza M. Ruzayqat, Neil K. Chada, Ajay Jasra

SIAM J. Sci. Comput., June, 2023

On a Dynamic Variant of the Iteratively Regularized Gauss-Newton Method with Sequential Data.

Neil K. Chada, Marco A. Iglesias, Shuai Lu, Frank Werner

SIAM J. Sci. Comput., June, 2023

Unbiased Kinetic Langevin Monte Carlo with Inexact Gradients.

Neil K. Chada, Benedict J. Leimkuhler, Daniel Paulin, Peter A. Whalley

CoRR, 2023

The Stochastic Steepest Descent Method for Robust Optimization in Banach Spaces.

Neil K. Chada, Philip J. Herbert

CoRR, 2023

2022

Cauchy Markov random field priors for Bayesian inversion.

Jarkko Suuronen, Neil K. Chada, Lassi Roininen

Stat. Comput., 2022

Multilevel estimation of normalization constants using ensemble Kalman-Bucy filters.

Hamza M. Ruzayqat, Neil K. Chada, Ajay Jasra

Stat. Comput., 2022

Convergence acceleration of ensemble Kalman inversion in nonlinear settings.

Neil K. Chada, Xin T. Tong

Math. Comput., 2022

Multilevel Ensemble Kalman-Bucy Filters.

Neil K. Chada, Ajay Jasra, Fangyuan Yu

SIAM/ASA J. Uncertain. Quantification, 2022

Improved Efficiency of Multilevel Monte Carlo for Stochastic PDE through Strong Pairwise Coupling.

Neil K. Chada, Håkon Hoel, Ajay Jasra, Georgios E. Zouraris

J. Sci. Comput., 2022

A Data-Adaptive Prior for Bayesian Learning of Kernels in Operators.

Neil K. Chada, Quanjun Lang, Fei Lu, Xiong Wang

CoRR, 2022

Unbiased Estimation of the Vanilla and Deterministic Ensemble Kalman-Bucy Filters.

Miguel Alvarez, Neil K. Chada, Ajay Jasra

CoRR, 2022

A Review of the EnKF for Parameter Estimation.

Neil K. Chada

CoRR, 2022

Multilevel Bayesian Deep Neural Networks.

Neil K. Chada, Ajay Jasra, Kody J. H. Law, Sumeetpal S. Singh

CoRR, 2022

2021

Unbiased Inference for Discretely Observed Hidden Markov Model Diffusions.

Neil K. Chada, Jordan Franks, Ajay Jasra, Kody J. H. Law, Matti Vihola

SIAM/ASA J. Uncertain. Quantification, 2021

Adaptive Tikhonov strategies for stochastic ensemble Kalman inversion.

Simon Weissmann, Neil K. Chada, Claudia Schillings, Xin T. Tong

CoRR, 2021

Unbiased Estimation of the Hessian for Partially Observed Diffusions.

Neil K. Chada, Ajay Jasra, Fangyuan Yu

CoRR, 2021

Multilevel Estimation of Normalization Constants Using the Ensemble Kalman-Bucy Filter.

Hamza M. Ruzayqat, Neil K. Chada, Ajay Jasra

CoRR, 2021

2020

Tikhonov Regularization within Ensemble Kalman Inversion.

Neil K. Chada, Andrew M. Stuart, Xin T. Tong

SIAM J. Numer. Anal., 2020

Iterative Ensemble Kalman Methods: A Unified Perspective with Some New Variants.

Neil K. Chada, Yuming Chen, Daniel Sanz-Alonso

CoRR, 2020

Consistency analysis of bilevel data-driven learning in inverse problems.

Neil K. Chada, Claudia Schillings, Xin T. Tong, Simon Weissmann

CoRR, 2020

2019

On the Incorporation of Box-Constraints for Ensemble Kalman Inversion.

Neil K. Chada, Claudia Schillings, Simon Weissmann

CoRR, 2019