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09/09/2023
Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in ReLU Networks
We consider the problem of performing Bayesian inference for logistic re...
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08/31/2023
Dropout Ensemble Kalman inversion for high dimensional inverse problems
Ensemble Kalman inversion (EKI) is an ensemble-based method to solve inv...
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04/25/2023
On forward-backward SDE approaches to continuous-time minimum variance estimation
The work of Kalman and Bucy has established a duality between filtering ...
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03/29/2023
EnKSGD: A Class Of Preconditioned Black Box Optimization And Inversion Algorithms
In this paper, we introduce the Ensemble Kalman-Stein Gradient Descent (...
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03/21/2023
Bayesian Dynamical Modeling of Fixational Eye Movements
Humans constantly move their eyes, even during visual fixations, where m...
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02/20/2023
Infinite-Dimensional Diffusion Models for Function Spaces
We define diffusion-based generative models in infinite dimensions, and ...
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09/23/2022
Ensemble Kalman Methods: A Mean Field Perspective
This paper provides a unifying mean field based framework for the deriva...
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09/12/2022
Data assimilation: A dynamic homotopy-based coupling approach
Homotopy approaches to Bayesian inference have found widespread use espe...
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04/09/2022
Efficient Derivative-free Bayesian Inference for Large-Scale Inverse Problems
We consider Bayesian inference for large scale inverse problems, where c...
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01/03/2022
Frequentist perspective on robust parameter estimation using the ensemble Kalman filter
Standard maximum likelihood or Bayesian approaches to parameter estimati...
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08/08/2021
Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations
We present a supervised learning method to learn the propagator map of a...
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04/16/2021
Affine-invariant ensemble transform methods for logistic regression
We investigate the application of ensemble transform approaches to Bayes...
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01/10/2021
Randomised maximum likelihood based posterior sampling
Minimization of a stochastic cost function is commonly used for approxim...
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07/24/2020
McKean-Vlasov SDEs in nonlinear filtering
Various particle filters have been proposed over the last couple of deca...
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07/14/2020
Supervised learning from noisy observations: Combining machine-learning techniques with data assimilation
Data-driven prediction and physics-agnostic machine-learning methods hav...
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06/03/2020
Spectral convergence of diffusion maps: improved error bounds and an alternative normalisation
Diffusion maps is a manifold learning algorithm widely used for dimensio...
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06/01/2020
Interacting particle solutions of Fokker-Planck equations through gradient-log-density estimation
Fokker-Planck equations are extensively employed in various scientific f...
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05/26/2020
GP-ETAS: Semiparametric Bayesian inference for the spatio-temporal Epidemic Type Aftershock Sequence model
The spatio-temporal Epidemic Type Aftershock Sequence (ETAS) model is wi...
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03/20/2020
Posterior contraction rates for non-parametric state and drift estimation
We consider a combined state and drift estimation problem for the linear...
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12/05/2019
Affine invariant interacting Langevin dynamics for Bayesian inference
We propose a computational method (with acronym ALDI) for sampling from ...
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11/25/2019
Fokker-Planck particle systems for Bayesian inference: Computational approaches
Bayesian inference can be embedded into an appropriately defined dynamic...
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08/28/2019
Note on Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler by Garbuno-Inigo, Hoffmann, Li and Stuart
An interacting system of Langevin dynamics driven particles has been pro...
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03/01/2019
Discrete gradients for computational Bayesian inference
In this paper, we exploit the gradient flow structure of continuous-time...
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01/18/2019
Algorithms for high-dimensional non-linear filtering and smoothing problems
Several numerical tools designed to overcome the challenges of smoothing...
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07/27/2018
