28 citations · 65 across the 11 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…
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms
Marco F. Cusumano-Towner, Vikash K. Mansinghka
Approximate probabilistic inference algorithms are central to many fields. Examples include sequential Monte Carlo inference in robotics, variational inference in machine learning,…
Probabilistic programs for inferring the goals of autonomous agents
Marco F. Cusumano-Towner, Alexey Radul, David Wingate +1
Intelligent systems sometimes need to infer the probable goals of people, cars, and robots, based on partial observations of their motion. This paper introduces a class of probabil…
Encapsulating models and approximate inference programs in probabilistic modules
Marco F. Cusumano-Towner, Vikash K. Mansinghka
This paper introduces the probabilistic module interface, which allows encapsulation of complex probabilistic models with latent variables alongside custom stochastic approximate i…
Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming
Marco F. Cusumano-Towner, Vikash K. Mansinghka
A key limitation of sampling algorithms for approximate inference is that it is difficult to quantify their approximation error. Widely used sampling schemes, such as sequential im…
Quantifying the probable approximation error of probabilistic inference programs
Marco F Cusumano-Towner, Vikash K Mansinghka
This paper introduces a new technique for quantifying the approximation error of a broad class of probabilistic inference programs, including ones based on both variational and Mon…