research
∙
08/02/2022
Bayesian Variable Selection in a Million Dimensions
Bayesian variable selection is a powerful tool for data analysis, as it ...
share
research
∙
12/22/2021
Surrogate Likelihoods for Variational Annealed Importance Sampling
Variational inference is a powerful paradigm for approximate Bayesian in...
research
∙
06/28/2021
Fast Bayesian Variable Selection in Binomial and Negative Binomial Regression
Bayesian variable selection is a powerful tool for data analysis, as it ...
research
∙
05/24/2021
Scalable Cross Validation Losses for Gaussian Process Models
We introduce a simple and scalable method for training Gaussian process ...
research
∙
02/27/2021
High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces
Bayesian optimization (BO) is a powerful paradigm for efficient optimiza...
research
∙
06/19/2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Matrix square roots and their inverses arise frequently in machine learn...
research
∙
02/21/2020
Deep Sigma Point Processes
We introduce Deep Sigma Point Processes, a class of parametric models in...
research
∙
12/24/2019
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro
NumPyro is a lightweight library that provides an alternate NumPy backen...
research
∙
11/01/2019
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
We introduce a fully stochastic gradient based approach to Bayesian opti...
research
∙
10/23/2019
Functional Tensors for Probabilistic Programming
It is a significant challenge to design probabilistic programming system...
research
∙
10/16/2019
Sparse Gaussian Process Regression Beyond Variational Inference
The combination of inducing point methods with stochastic variational in...
research
∙
05/31/2019
Neural Likelihoods for Multi-Output Gaussian Processes
We construct flexible likelihoods for multi-output Gaussian process mode...
research
∙
03/13/2019
Variational Estimators for Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework fo...
research
∙
03/13/2019
Variational Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework fo...
research
∙
02/08/2019
Tensor Variable Elimination for Plated Factor Graphs
A wide class of machine learning algorithms can be reduced to variable e...
research
∙
11/02/2018
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...
research
∙
10/18/2018
Pyro: Deep Universal Probabilistic Programming
Pyro is a probabilistic programming language built on Python as a platfo...
research
∙
06/05/2018
Pathwise Derivatives for Multivariate Distributions
We exploit the link between the transport equation and derivatives of ex...
research
∙
06/05/2018