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01/26/2023
Random Grid Neural Processes for Parametric Partial Differential Equations
We introduce a new class of spatially stochastic physics and data inform...
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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...
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10/29/2022
Ice Core Dating using Probabilistic Programming
Ice cores record crucial information about past climate. However, before...
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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...
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10/29/2021
Aligned Multi-Task Gaussian Process
Multi-task learning requires accurate identification of the correlations...
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10/22/2021
Variational Bayesian Approximation of Inverse Problems using Sparse Precision Matrices
Inverse problems involving partial differential equations (PDEs) are wid...
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01/27/2020
Bayesian nonparametric shared multi-sequence time series segmentation
In this paper, we introduce a method for segmenting time series data usi...
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09/17/2019
Compositional uncertainty in deep Gaussian processes
Gaussian processes (GPs) are nonparametric priors over functions, and fi...
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06/26/2019
Modulated Bayesian Optimization using Latent Gaussian Process Models
We present an approach to Bayesian Optimization that allows for robust s...
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05/30/2019
Monotonic Gaussian Process Flow
We propose a new framework of imposing monotonicity constraints in a Bay...
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11/26/2018
Sequence Alignment with Dirichlet Process Mixtures
We present a probabilistic model for unsupervised alignment of high-dime...
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03/07/2018