83 citations · 126 across the 30 of their papers we have counts for
36 papers
Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology
Neil K. Chada, Simon Cotter, Kody J. H. Law +1
Reliable uncertainty quantification remains an important challenge in AI. Hierarchical Bayesian methods provide a principled framework for this by representing unknown quantities t…
Information-Theoretic Foundations of Online Temperature Sampling
Jin Sima, Nikolaos Papagiannis, Ananth Grama +1
Repetitive, degenerate generation is a persistent failure of LLM decoding, and the standard remedies (temperature, nucleus, and top- sampling) are static heuristics. By Kac's le…
Robustness of Diffusion Models under Distribution Shift
Wei Luo, Neil K. Chada, Shijie Zhang +1
Score-based diffusion models are increasingly considered in settings where the underlying data distribution may differ from the training distribution, yet existing theoretical guar…
Kinetic Langevin Splitting Schemes for Constrained Sampling
Neil K. Chada, Lu Yu
Constrained sampling is an important and challenging task in computational statistics, concerned with generating samples from a distribution under certain constraints. There are nu…
Multilevel Localized Ensemble Kalman Bucy Filters
Neil K. Chada
In this article we propose and develop a new methodology which is inspired from Kalman filtering and multilevel Monte Carlo (MLMC), entitle the multilevel localized ensemble Kalman…
Unbiased Parameter Estimation for Bayesian Inverse Problems
Neil K. Chada, Ajay Jasra, Mohamed Maama +1
In this paper we consider the estimation of unknown parameters in Bayesian inverse problems. In most cases of practical interest, there are several barriers to performing such esti…
Unbiased Approximations for Stationary Distributions of McKean-Vlasov SDEs
Elsiddig Awadelkarim, Neil K. Chada, Ajay Jasra
We consider the development of unbiased estimators, to approximate the stationary distribution of Mckean-Vlasov stochastic differential equations (MVSDEs). These are an important c…
Learning dynamical systems from data: Gradient-based dictionary optimization
Mohammad Tabish, Neil K. Chada, Stefan Klus
The Koopman operator plays a crucial role in analyzing the global behavior of dynamical systems. Existing data-driven methods for approximating the Koopman operator or discovering…
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics
Daniel Paulin, Peter A. Whalley, Neil K. Chada +1
We propose a scalable kinetic Langevin dynamics algorithm for sampling parameter spaces of big data and AI applications. Our scheme combines a symmetric forward/backward sweep over…
A Stochastic Iteratively Regularized Gauss-Newton Method
El Houcine Bergou, Neil K. Chada, Youssef Diouane
This work focuses on developing and motivating a stochastic version of a wellknown inverse problem methodology. Specifically, we consider the iteratively regularized Gauss-Newton m…
The Ensemble Kalman Filter for Dynamic Inverse Problems
Simon Weissmann, Neil K. Chada, Xin T. Tong
In inverse problems, the goal is to estimate unknown model parameters from noisy observational data. Traditionally, inverse problems are solved under the assumption of a fixed forw…
Unbiased Kinetic Langevin Monte Carlo with Inexact Gradients
Neil K. Chada, Benedict Leimkuhler, Daniel Paulin +1
We present an unbiased method for Bayesian posterior means based on kinetic Langevin dynamics that combines advanced splitting methods with enhanced gradient approximations. Our ap…
A Statistical Framework and Analysis for Perfect Radar Pulse Compression
Neil K. Chada, Petteri Piiroinen, Lassi Roininen
Perfect radar pulse compression coding is a potential emerging field which aims at providing rigorous analysis and fundamental limit radar experiments. It is based on finding non-t…
The Stochastic Steepest Descent Method for Robust Optimization in Banach Spaces
Neil K. Chada, Philip J. Herbert
Stochastic gradient methods have been a popular and powerful choice of optimization methods, aimed at minimizing functions. Their advantage lies in the fact that that one approxima…
A Data-Adaptive Prior for Bayesian Learning of Kernels in Operators
Neil K. Chada, Quanjun Lang, Fei Lu +1
Kernels are efficient in representing nonlocal dependence and they are widely used to design operators between function spaces. Thus, learning kernels in operators from data is an…
Bayesian inversion with α-stable priors
Jarkko Suuronen, Tomás Soto, Neil K. Chada +1
We propose to use Lévy α-stable distributions for constructing priors for Bayesian inverse problems. The construction is based on Markov fields with stable-distributed increments.…
Unbiased Estimation of the Vanilla and Deterministic Ensemble Kalman-Bucy Filters
Miguel Alvarez, Neil K. Chada, Ajay Jasra
In this article we consider the development of an unbiased estimator for the ensemble Kalman--Bucy filter (EnKBF). The EnKBF is a continuous-time filtering methodology which can be…
On a Dynamic Variant of the Iteratively Regularized Gauss-Newton Method with Sequential Data
Neil K. Chada, Marco A. Iglesias, Shuai Lu +1
For numerous parameter and state estimation problems, assimilating new data as they become available can help produce accurate and fast inference of unknown quantities. While most…
A Review of the EnKF for Parameter Estimation
Neil K. Chada
The ensemble Kalman filter is a well-known and celebrated data assimilation algorithm. It is of particular relevance as it used for high-dimensional problems, by updating an ensemb…
Unbiased Estimation using Underdamped Langevin Dynamics
Hamza Ruzayqat, Neil K. Chada, Ajay Jasra
In this work we consider the unbiased estimation of expectations w.r.t.~probability measures that have non-negative Lebesgue density, and which are known point-wise up-to a normali…
Bayesian Deep Learning with Multilevel Trace-class Neural Networks
Neil K. Chada, Ajay Jasra, Kody J. H. Law +1
In this article we consider Bayesian inference associated to deep neural networks (DNNs) and in particular, trace-class neural network (TNN) priors which can be preferable to tradi…
Adaptive Tikhonov strategies for stochastic ensemble Kalman inversion
Simon Weissmann, Neil K. Chada, Claudia Schillings +1
Ensemble Kalman inversion (EKI) is a derivative-free optimizer aimed at solving inverse problems, taking motivation from the celebrated ensemble Kalman filter. The purpose of this…
Unbiased Estimation of the Hessian for Partially Observed Diffusions
Neil K. Chada, Ajay Jasra, Fangyuan Yu
In this article we consider the development of unbiased estimators of the Hessian, of the log-likelihood function with respect to parameters, for partially observed diffusion proce…
Multilevel Estimation of Normalization Constants Using the Ensemble Kalman-Bucy Filter
Hamza Ruzayqat, Neil K. Chada, Ajay Jasra
In this article we consider the application of multilevel Monte Carlo, for the estimation of normalizing constants. In particular we will make use of the filtering algorithm, the e…