572 citations · 2k across the 30 of their papers we have counts for
30 papers
Reparameterized Variational Rejection Sampling
Martin Jankowiak, Du Phan
Traditional approaches to variational inference rely on parametric families of variational distributions, with the choice of family playing a critical role in determining the accur…
Bayesian Variable Selection in a Million Dimensions
Martin Jankowiak
Bayesian variable selection is a powerful tool for data analysis, as it offers a principled method for variable selection that accounts for prior information and uncertainty. Howev…
Surrogate Likelihoods for Variational Annealed Importance Sampling
Martin Jankowiak, Du Phan
Variational inference is a powerful paradigm for approximate Bayesian inference with a number of appealing properties, including support for model learning and data subsampling. By…
Fast Bayesian Variable Selection in Binomial and Negative Binomial Regression
Martin Jankowiak
Bayesian variable selection is a powerful tool for data analysis, as it offers a principled method for variable selection that accounts for prior information and uncertainty. Howev…
Scalable Cross Validation Losses for Gaussian Process Models
Martin Jankowiak, Geoff Pleiss
We introduce a simple and scalable method for training Gaussian process (GP) models that exploits cross-validation and nearest neighbor truncation. To accommodate binary and multi-…
High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces
David Eriksson, Martin Jankowiak
Bayesian optimization (BO) is a powerful paradigm for efficient optimization of black-box objective functions. High-dimensional BO presents a particular challenge, in part because…