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...
share
research
∙
11/26/2022
Optimisation of a global climate model ensemble for prediction of extreme heat days
Adaptation-relevant predictions of climate change are often derived by c...
research
∙
10/29/2022
Ice Core Dating using Probabilistic Programming
Ice cores record crucial information about past climate. However, before...
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
∙
10/29/2021
Aligned Multi-Task Gaussian Process
Multi-task learning requires accurate identification of the correlations...
research
∙
10/22/2021
Variational Bayesian Approximation of Inverse Problems using Sparse Precision Matrices
Inverse problems involving partial differential equations (PDEs) are wid...
research
∙
01/27/2020
Bayesian nonparametric shared multi-sequence time series segmentation
In this paper, we introduce a method for segmenting time series data usi...
research
∙
09/17/2019
Compositional uncertainty in deep Gaussian processes
Gaussian processes (GPs) are nonparametric priors over functions, and fi...
research
∙
06/26/2019
Modulated Bayesian Optimization using Latent Gaussian Process Models
We present an approach to Bayesian Optimization that allows for robust s...
research
∙
05/30/2019
Monotonic Gaussian Process Flow
We propose a new framework of imposing monotonicity constraints in a Bay...
research
∙
11/26/2018
Sequence Alignment with Dirichlet Process Mixtures
We present a probabilistic model for unsupervised alignment of high-dime...
research
∙
03/07/2018
