
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Matrix square roots and their inverses arise frequently in machine learn...
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Deep Sigma Point Processes
We introduce Deep Sigma Point Processes, a class of parametric models in...
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Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro
NumPyro is a lightweight library that provides an alternate NumPy backen...
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A Unified Stochastic Gradient Approach to Designing BayesianOptimal Experiments
We introduce a fully stochastic gradient based approach to Bayesian opti...
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Functional Tensors for Probabilistic Programming
It is a significant challenge to design probabilistic programming system...
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Sparse Gaussian Process Regression Beyond Variational Inference
The combination of inducing point methods with stochastic variational in...
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Neural Likelihoods for MultiOutput Gaussian Processes
We construct flexible likelihoods for multioutput Gaussian process mode...
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Variational Estimators for Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework fo...
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Variational Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework fo...
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Tensor Variable Elimination for Plated Factor Graphs
A wide class of machine learning algorithms can be reduced to variable e...
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Closed Form Variational Objectives For Bayesian Neural Networks with a Single Hidden Layer
In this note we consider setups in which variational objectives for Baye...
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Pyro: Deep Universal Probabilistic Programming
Pyro is a probabilistic programming language built on Python as a platfo...
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Pathwise Derivatives for Multivariate Distributions
We exploit the link between the transport equation and derivatives of ex...
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Pathwise Derivatives Beyond the Reparameterization Trick
We observe that gradients computed via the reparameterization trick are ...
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Martin Jankowiak
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