Neil K. Chada | DeepAI

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∙ 08/15/2023

A Statistical Framework and Analysis for Perfect Radar Pulse Compression

Perfect radar pulse compression coding is a potential emerging field whi...

0 Neil K. Chada, et al. ∙

∙ 08/11/2023

The Stochastic Steepest Descent Method for Robust Optimization in Banach Spaces

Stochastic gradient methods have been a popular and powerful choice of o...

0 Neil K. Chada, et al. ∙

∙ 12/29/2022

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

Kernels are efficient in representing nonlocal dependence and they are w...

0 Neil K. Chada, et al. ∙

∙ 12/11/2022

Bayesian inversion with α-stable priors

We propose to use Lévy α-stable distributions for constructing priors fo...

0 Jarkko Suuronen, et al. ∙

∙ 08/08/2022

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

In this article we consider the development of an unbiased estimator for...

0 Miguel Álvarez, et al. ∙

∙ 07/27/2022

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

For numerous parameter and state estimation problems, assimilating new d...

0 Neil K. Chada, et al. ∙

∙ 07/26/2022

A Review of the EnKF for Parameter Estimation

The ensemble Kalman filter is a well-known and celebrated data assimilat...

0 Neil K. Chada, et al. ∙

∙ 06/14/2022

Unbiased Estimation using the Underdamped Langevin Dynamics

In this work we consider the unbiased estimation of expectations w.r.t. ...

0 Hamza Ruzayqat, et al. ∙

∙ 03/24/2022

Multilevel Bayesian Deep Neural Networks

In this article we consider Bayesian inference associated to deep neural...

0 Neil K. Chada, et al. ∙

∙ 10/18/2021

Adaptive Tikhonov strategies for stochastic ensemble Kalman inversion

Ensemble Kalman inversion (EKI) is a derivative-free optimizer aimed at ...

0 Simon Weissmann, et al. ∙

∙ 09/06/2021

Unbiased Estimation of the Hessian for Partially Observed Diffusions

In this article we consider the development of unbiased estimators of th...

0 Neil K. Chada, et al. ∙

∙ 08/09/2021

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

In this article we consider the application of multilevel Monte Carlo, f...

0 Hamza Ruzayqat, et al. ∙

∙ 08/02/2021

Improved efficiency of multilevel Monte Carlo for stochastic PDE through strong pairwise coupling

Multilevel Monte Carlo (MLMC) has become an important methodology in app...

0 Neil K. Chada, et al. ∙

∙ 05/26/2021

Cauchy Markov Random Field Priors for Bayesian Inversion

The use of Cauchy Markov random field priors in statistical inverse prob...

0 Neil K. Chada, et al. ∙

∙ 11/09/2020

Multilevel Ensemble Kalman-Bucy Filters

In this article we consider the linear filtering problem in continuous-t...

0 Neil K. Chada, et al. ∙

∙ 10/26/2020

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

This paper provides a unified perspective of iterative ensemble Kalman m...

0 Neil K. Chada, et al. ∙

∙ 07/06/2020

Consistency analysis of bilevel data-driven learning in inverse problems

One fundamental problem when solving inverse problems is how to find reg...

0 Neil K. Chada, et al. ∙

∙ 11/06/2019

Convergence Acceleration of Ensemble Kalman Inversion in Nonlinear Settings

Many data-science problems can be formulated as an inverse problem, wher...

0 Neil K. Chada, et al. ∙

∙ 08/12/2019

Elements of asymptotic theory with outer probability measures

Outer measures can be used for statistical inference in place of probabi...

0 Jeremie Houssineau, et al. ∙

∙ 08/02/2019

On the Incorporation of Box-Constraints for Ensemble Kalman Inversion

The Bayesian approach to inverse problems is widely used in practice to ...

0 Neil K. Chada, et al. ∙

∙ 07/06/2019

Posterior Convergence Analysis of α-Stable Sheets

This paper is concerned with the theoretical understanding of α-stable s...

0 Neil K. Chada, et al. ∙

∙ 07/06/2019

Posterior Convergence of α-Stable Sheets

This paper is concerned with the theoretical understanding of α-stable s...

0 Neil K. Chada, et al. ∙

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