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08/18/2023
Accelerated Bayesian imaging by relaxed proximal-point Langevin sampling
This paper presents a new accelerated proximal Markov chain Monte Carlo ...
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07/17/2023
Gaussian processes for Bayesian inverse problems associated with linear partial differential equations
This work is concerned with the use of Gaussian surrogate models for Bay...
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06/28/2022
The split Gibbs sampler revisited: improvements to its algorithmic structure and augmented target distribution
This paper proposes a new accelerated proximal Markov chain Monte Carlo ...
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06/10/2022
Efficient Bayesian computation for low-photon imaging problems
This paper studies a new and highly efficient Markov chain Monte Carlo (...
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11/10/2021
A Hierarchy of Network Models Giving Bistability Under Triadic Closure
Triadic closure describes the tendency for new friendships to form betwe...
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04/26/2021
Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations
We present a framework that allows for the non-asymptotic study of the 2...
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03/18/2021
Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithms
This paper proposes a new methodology for performing Bayesian inference ...
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09/23/2020
A Linear Transportation L^p Distance for Pattern Recognition
The transportation L^p distance, denoted TL^p, has been proposed as a ge...
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09/01/2020
The connections between Lyapunov functions for some optimization algorithms and differential equations
In this manuscript we study the properties of a family of a second order...
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11/11/2019
Constructing Gradient Controllable Recurrent Neural Networks Using Hamiltonian Dynamics
Recurrent neural networks (RNNs) have gained a great deal of attention i...
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09/23/2019
PDE-Inspired Algorithms for Semi-Supervised Learning on Point Clouds
Given a data set and a subset of labels the problem of semi-supervised l...
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09/22/2019
Contractivity of Runge-Kutta methods for convex gradient systems
We consider the application of Runge-Kutta (RK) methods to gradient syst...
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08/23/2019
Accelerating proximal Markov chain Monte Carlo by using explicit stabilised methods
We present a highly efficient proximal Markov chain Monte Carlo methodol...
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03/26/2017
Uncertainty Quantification in the Classification of High Dimensional Data
Classification of high dimensional data finds wide-ranging applications....
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01/02/2015