572 citations · 2k across the 30 of their papers we have counts for
7 papers · 1 filter
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…
Functional Tensors for Probabilistic Programming
Fritz Obermeyer, Eli Bingham, Martin Jankowiak +2
It is a significant challenge to design probabilistic programming systems that can accommodate a wide variety of inference strategies within a unified framework. Noting that the ve…
Parametric Gaussian Process Regressors
Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
The combination of inducing point methods with stochastic variational inference has enabled approximate Gaussian Process (GP) inference on large datasets. Unfortunately, the result…
Neural Likelihoods for Multi-Output Gaussian Processes
Martin Jankowiak, Jacob Gardner
We construct flexible likelihoods for multi-output Gaussian process models that leverage neural networks as components. We make use of sparse variational inference methods to enabl…
Variational Bayesian Optimal Experimental Design
Adam Foster, Martin Jankowiak, Eli Bingham +4
Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, its applicability is hampered by th…