As the use of machine learning (ML) models in product development and data-driven
decision-making processes became pervasive in many domains, people's focus on building …

We present new techniques for automatically constructing probabilistic programs for data
analysis, interpretation, and prediction. These techniques work with probabilistic domain …

We present the Sum-Product Probabilistic Language (SPPL), a new probabilistic
programming language that automatically delivers exact solutions to a broad range of …

We show that streams and lazy data structures are a natural idiom for programming with
infinite-dimensional Bayesian methods such as Poisson processes, Gaussian processes …

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 …

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 …

This article proposes a Bayesian nonparametric method for forecasting, imputation, and
clustering in sparsely observed, multivariate time series data. The method is appropriate for …

This discussion paper presents a conversation between researchers having active interests
in the usability of probabilistic programming languages (PPLs), but coming from a wide …

A fundamental task in AI is to assess (in) dependence between mixed-type variables (text,
image, sound). We propose a Bayesian kernelised correlation test of (in) dependence using …

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 …