490 citations · 1k across the 74 of their papers we have counts for
7 papers · 1 filter
Sequential Monte Carlo Learning for Time Series Structure Discovery
Feras A. Saad, Brian J. Patton, Matthew D. Hoffman +2
This paper presents a new approach to automatically discovering accurate models of complex time series data. Working within a Bayesian nonparametric prior over a symbolic space of…
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…
Hierarchical Infinite Relational Model
Feras A. Saad, Vikash K. Mansinghka
This paper describes the hierarchical infinite relational model (HIRM), a new probabilistic generative model for noisy, sparse, and heterogeneous relational data. Given a set of re…
SBI: A Simulation-Based Test of Identifiability for Bayesian Causal Inference
Sam Witty, David Jensen, Vikash Mansinghka
A growing family of approaches to causal inference rely on Bayesian formulations of assumptions that go beyond causal graph structure. For example, Bayesian approaches have been de…
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming
Alexander K. Lew, Monica Agrawal, David Sontag +1
Data cleaning is naturally framed as probabilistic inference in a generative model of ground-truth data and likely errors, but the diversity of real-world error patterns and the ha…
Probabilistic Programming with Gaussian Process Memoization
Ulrich Schaechtle, Ben Zinberg, Alexey Radul +2
Gaussian Processes (GPs) are widely used tools in statistics, machine learning, robotics, computer vision, and scientific computation. However, despite their popularity, they can b…