572 citations · 2k across the 30 of their papers we have counts for
14 papers · 1 filter
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…
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…
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-…
Deep Sigma Point Processes
Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
We introduce Deep Sigma Point Processes, a class of parametric models inspired by the compositional structure of Deep Gaussian Processes (DGPs). Deep Sigma Point Processes (DSPPs)…
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro
Du Phan, Neeraj Pradhan, Martin Jankowiak
NumPyro is a lightweight library that provides an alternate NumPy backend to the Pyro probabilistic programming language with the same modeling interface, language primitives and e…
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
Adam Foster, Martin Jankowiak, Matthew O'Meara +2
We introduce a fully stochastic gradient based approach to Bayesian optimal experimental design (BOED). Our approach utilizes variational lower bounds on the expected information g…