On the Incorporation of Box-Constraints for Ensemble Kalman Inversion. - Neil K. Chada; Claudia Schillings; Simon Weissmann (CoRR, 2019)

Consistency analysis of bilevel data-driven learning in inverse problems. - Neil K. Chada; Claudia Schillings; Xin T. Tong; Simon Weissmann (CoRR, 2020)

Iterative Ensemble Kalman Methods: A Unified Perspective with Some New Variants. - Neil K. Chada; Yuming Chen; Daniel Sanz-Alonso (CoRR, 2020)

Tikhonov Regularization within Ensemble Kalman Inversion. - Neil K. Chada; Andrew M. Stuart; Xin T. Tong (SIAM J. Numer. Anal., 2020)

Multilevel Estimation of Normalization Constants Using the Ensemble Kalman-Bucy Filter. - Hamza M. Ruzayqat; Neil K. Chada; Ajay Jasra (CoRR, 2021)

Unbiased Estimation of the Hessian for Partially Observed Diffusions. - Neil K. Chada; Ajay Jasra; Fangyuan Yu (CoRR, 2021)

Adaptive Tikhonov strategies for stochastic ensemble Kalman inversion. - Simon Weissmann; Neil K. Chada; Claudia Schillings; Xin T. Tong (CoRR, 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)

Multilevel Bayesian Deep Neural Networks. - Neil K. Chada; Ajay Jasra; Kody J. H. Law; Sumeetpal S. Singh (CoRR, 2022)

A Review of the EnKF for Parameter Estimation. - Neil K. Chada (CoRR, 2022)

Unbiased Estimation of the Vanilla and Deterministic Ensemble Kalman-Bucy Filters. - Miguel Alvarez; Neil K. Chada; Ajay Jasra (CoRR, 2022)

A Data-Adaptive Prior for Bayesian Learning of Kernels in Operators. - Neil K. Chada; Quanjun Lang; Fei Lu 016; Xiong Wang (CoRR, 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)

Multilevel Ensemble Kalman-Bucy Filters. - Neil K. Chada; Ajay Jasra; Fangyuan Yu (SIAM/ASA J. Uncertain. Quantification, 2022)

Convergence acceleration of ensemble Kalman inversion in nonlinear settings. - Neil K. Chada; Xin T. Tong (Math. Comput., 2022)

Multilevel estimation of normalization constants using ensemble Kalman-Bucy filters. - Hamza M. Ruzayqat; Neil K. Chada; Ajay Jasra (Stat. Comput., 2022)

Cauchy Markov random field priors for Bayesian inversion. - Jarkko Suuronen; Neil K. Chada; Lassi Roininen (Stat. Comput., 2022)

The Stochastic Steepest Descent Method for Robust Optimization in Banach Spaces. - Neil K. Chada; Philip J. Herbert (CoRR, 2023)

Unbiased Kinetic Langevin Monte Carlo with Inexact Gradients. - Neil K. Chada; Benedict J. Leimkuhler; Daniel Paulin; Peter A. Whalley (CoRR, 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 002 (SIAM J. Sci. Comput., 2023)

Unbiased Estimation Using Underdamped Langevin Dynamics. - Hamza M. Ruzayqat; Neil K. Chada; Ajay Jasra (SIAM J. Sci. Comput., 2023)

The Ensemble Kalman Filter for Dynamic Inverse Problems. - Simon Weissmann; Neil K. Chada; Xin T. Tong (CoRR, 2024)

A Stochastic Iteratively Regularized Gauss-Newton Method. - El Houcine Bergou; Neil K. Chada; Youssef Diouane (CoRR, 2024)

Learning dynamical systems from data: Gradient-based dictionary optimization. - Mohammad Tabish; Neil K. Chada; Stefan Klus (CoRR, 2024)

Unbiased Approximations for Stationary Distributions of McKean-Vlasov SDEs. - Elsiddig Awadelkarim; Neil K. Chada; Ajay Jasra (CoRR, 2024)

Decoupling epistemic and aleatoric uncertainties with possibility theory. - Nong Minh Hieu; Jeremie Houssineau; Neil K. Chada; Emmanuel Delande (AISTATS, 2025)

Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics. - Daniel Paulin; Peter A. Whalley; Neil K. Chada; Benedict J. Leimkuhler (AISTATS, 2025)