28 citations · 45 across the 5 of their papers we have counts for
11 papers
Recursive Monte Carlo and Variational Inference with Auxiliary Variables
Alexander K. Lew, Marco Cusumano-Towner, Vikash K. Mansinghka
A key design constraint when implementing Monte Carlo and variational inference algorithms is that it must be possible to cheaply and exactly evaluate the marginal densities of pro…
Estimators of Entropy and Information via Inference in Probabilistic Models
Feras A. Saad, Marco Cusumano-Towner, Vikash K. Mansinghka
Estimating information-theoretic quantities such as entropy and mutual information is central to many problems in statistics and machine learning, but challenging in high dimension…
3DP3: 3D Scene Perception via Probabilistic Programming
Nishad Gothoskar, Marco Cusumano-Towner, Ben Zinberg +6
We present 3DP3, a framework for inverse graphics that uses inference in a structured generative model of objects, scenes, and images. 3DP3 uses (i) voxel models to represent the 3…
Automating Involutive MCMC using Probabilistic and Differentiable Programming
Marco Cusumano-Towner, Alexander K. Lew, Vikash K. Mansinghka
Involutive MCMC is a unifying mathematical construction for MCMC kernels that generalizes many classic and state-of-the-art MCMC algorithms, from reversible jump MCMC to kernels ba…
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling
Feras A. Saad, Marco F. Cusumano-Towner, Ulrich Schaechtle +2
We present new techniques for automatically constructing probabilistic programs for data analysis, interpretation, and prediction. These techniques work with probabilistic domain-s…
Using probabilistic programs as proposals
Marco F. Cusumano-Towner, Vikash K. Mansinghka
Monte Carlo inference has asymptotic guarantees, but can be slow when using generic proposals. Handcrafted proposals that rely on user knowledge about the posterior distribution ca…