Search Results for author:
Found 11 papers, 4 papers with code
Random Grid Neural Processes for Parametric Partial Differential Equations
We introduce a new class of spatially stochastic physics and data informed deep latent models for parametric partial differential equations (PDEs) which operate through scalable variational neural processes.
A locally time-invariant metric for climate model ensemble predictions of extreme risk
Adaptation-relevant predictions of climate change are often derived by combining climate model simulations in a multi-model ensemble.
Ice Core Dating using Probabilistic Programming
Ice cores record crucial information about past climate.
Fully probabilistic deep models for forward and inverse problems in parametric PDEs
In the posited probabilistic model, both the forward and inverse maps are approximated as Gaussian distributions with a mean and covariance parameterized by deep neural networks.
Aligned Multi-Task Gaussian Process
Building on the previous work by Kazlauskaiteet al. [2019], we include a separate monotonic warp of the input data to model temporal misalignment.
Bayesian nonparametric shared multi-sequence time series segmentation
In this paper, we introduce a method for segmenting time series data using tools from Bayesian nonparametrics.
Compositional uncertainty in deep Gaussian processes
Similarly, deep Gaussian processes (DGPs) should allow us to compute a posterior distribution of compositions of multiple functions giving rise to the observations.
Modulating Surrogates for Bayesian Optimization
Bayesian optimization (BO) methods often rely on the assumption that the objective function is well-behaved, but in practice, this is seldom true for real-world objectives even if noise-free observations can be collected.
Monotonic Gaussian Process Flow
We propose a new framework for imposing monotonicity constraints in a Bayesian nonparametric setting based on numerical solutions of stochastic differential equations.
Sequence Alignment with Dirichlet Process Mixtures
We present a probabilistic model for unsupervised alignment of high-dimensional time-warped sequences based on the Dirichlet Process Mixture Model (DPMM).
Gaussian Process Latent Variable Alignment Learning
We present a model that can automatically learn alignments between high-dimensional data in an unsupervised manner.
