490 citations · 1k across the 74 of their papers we have counts for
13 papers · 1 filter
Differentiating Metropolis-Hastings to Optimize Intractable Densities
Gaurav Arya, Ruben Seyer, Frank Schäfer +7
We develop an algorithm for automatic differentiation of Metropolis-Hastings samplers, allowing us to differentiate through probabilistic inference, even if the model has discrete…
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…
Deep Involutive Generative Models for Neural MCMC
Span Spanbauer, Cameron Freer, Vikash Mansinghka
We introduce deep involutive generative models, a new architecture for deep generative modeling, and use them to define Involutive Neural MCMC, a new approach to fast neural MCMC.…
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,…
Time Series Structure Discovery via Probabilistic Program Synthesis
Ulrich Schaechtle, Feras Saad, Alexey Radul +1
There is a widespread need for techniques that can discover structure from time series data. Recently introduced techniques such as Automatic Bayesian Covariance Discovery (ABCD) p…
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes
Feras Saad, Vikash Mansinghka
Datasets with hundreds of variables and many missing values are commonplace. In this setting, it is both statistically and computationally challenging to detect true predictive rel…