research
∙
07/18/2023
Adaptively Optimised Adaptive Importance Samplers
We introduce a new class of adaptive importance samplers leveraging adap...
share
research
∙
01/26/2023
Random Grid Neural Processes for Parametric Partial Differential Equations
We introduce a new class of spatially stochastic physics and data inform...
research
∙
10/19/2022
Gradient-based Adaptive Importance Samplers
Importance sampling (IS) is a powerful Monte Carlo methodology for the a...
research
∙
08/09/2022
Deep Probabilistic Models for Forward and Inverse Problems in Parametric PDEs
We formulate a class of physics-driven deep latent variable models (PDDL...
research
∙
01/02/2022
Global convergence of optimized adaptive importance samplers
We analyze the optimized adaptive importance sampler (OAIS) for performi...
research
∙
10/21/2021
Statistical Finite Elements via Langevin Dynamics
The recent statistical finite element method (statFEM) provides a cohere...
research
∙
10/20/2020
VarGrad: A Low-Variance Gradient Estimator for Variational Inference
We analyse the properties of an unbiased gradient estimator of the ELBO ...
research
∙
02/23/2020
Generalized Bayesian Filtering via Sequential Monte Carlo
We introduce a framework for inference in general state-space hidden Mar...
research
∙
02/13/2020
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization
We provide a nonasymptotic analysis of the convergence of the stochastic...
research
∙
10/09/2019
Probabilistic sequential matrix factorization
We introduce the probabilistic sequential matrix factorization (PSMF) me...
research
∙
10/04/2019
Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization
Within the context of empirical risk minimization, see Raginsky, Rakhlin...
research
∙
03/28/2019
Convergence rates for optimised adaptive importance samplers
Adaptive importance samplers are adaptive Monte Carlo algorithms to esti...
research
∙
12/04/2018
A probabilistic incremental proximal gradient method
In this paper, we propose a probabilistic optimization method, named pro...
research
∙
11/23/2018
Parallel sequential Monte Carlo for stochastic optimization
We propose a parallel sequential Monte Carlo optimization method to mini...
research
∙
07/12/2018
The Incremental Proximal Method: A Probabilistic Perspective
In this work, we highlight a connection between the incremental proximal...
research
∙
12/21/2017
A probabilistic interpretation of replicator-mutator dynamics
In this note, we investigate the relationship between probabilistic upda...
research
∙
08/25/2017
Nudging the Particle Filter
We investigate a new sampling scheme to improve the performance of parti...
research
∙
09/07/2015
Matrix Factorisation with Linear Filters
This text investigates relations between two well-known family of algori...
research
∙
06/14/2015