572 citations · 657 across the 6 of their papers we have counts for
6 papers
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…
Tensor Variable Elimination for Plated Factor Graphs
Fritz Obermeyer, Eli Bingham, Martin Jankowiak +4
A wide class of machine learning algorithms can be reduced to variable elimination on factor graphs. While factor graphs provide a unifying notation for these algorithms, they do n…
Joint Mapping and Calibration via Differentiable Sensor Fusion
Jonathan P. Chen, Fritz Obermeyer, Vladimir Lyapunov +2
We leverage automatic differentiation (AD) and probabilistic programming to develop an end-to-end optimization algorithm for batch triangulation of a large number of unknown object…
Pyro: Deep Universal Probabilistic Programming
Eli Bingham, Jonathan P. Chen, Martin Jankowiak +7
Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large datasets and high-dimensio…
Pathwise Derivatives Beyond the Reparameterization Trick
Martin Jankowiak, Fritz Obermeyer
We observe that gradients computed via the reparameterization trick are in direct correspondence with solutions of the transport equation in the formalism of optimal transport. We…
Scaling Nonparametric Bayesian Inference via Subsample-Annealing
Fritz Obermeyer, Jonathan Glidden, Eric Jonas
We describe an adaptation of the simulated annealing algorithm to nonparametric clustering and related probabilistic models. This new algorithm learns nonparametric latent structur…