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 …

This article presents GenSQL, a probabilistic programming system for querying probabilistic
generative models of database tables. By augmenting SQL with only a few key primitives for …

In this paper, we investigate the use of probabilistic graphical models, specifically stochastic
blockmodels, for the purpose of hierarchical entity clustering on knowledge graphs. These …

This paper proposes the permuton-induced Chinese restaurant process (PCRP), a
stochastic process on rectangular partitioning of a matrix. This distribution is suitable for use …

This paper proposes a new inference strategy for Bayesian relational data analysis, inspired
by the sunflower lemma in extremal combinatorics. Relational data analysis using …

InferenceQL is a probabilistic programming system for scalable Bayesian AutoML from
database tables. InferenceQL is designed to help make Bayesian approaches to data …

How can we automate and scale up the processes of learning accurate probabilistic models
of complex data and obtaining principled solutions to probabilistic inference and analysis …

Abstract Knowledge graphs are data storage structures that rely on principles from graph
theory to represent information. Specifically, facts are stored as triples which bring together …