Recording sensor data is seldom a perfect process. Failures in power, communication or
storage can leave occasional blocks of data missing, affecting not only real-time monitoring …

Probabilistic programming languages (PPLs) are an expressive means of representing and
reasoning about probabilistic models. The computational challenge of probabilistic …

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

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 …

With the emergence of the Internet of Things (IoT), time series data has become ubiquitous
in our daily life. Making sense of time series is a topic of great interest in many domains …

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 …

We propose a novel tool, AquaSense, to automatically reason about the sensitivity analysis
of probabilistic programs. In the context of probabilistic programs, sensitivity analysis …

Probabilistic programming languages (PPLs) are an expressive means for creating and
reasoning about probabilistic models. Unfortunately hybrid probabilistic programs that …

A major bottleneck of the current Machine Learning (ML) workflow is the time consuming,
error prone engineering required to get data from a datastore or a database (DB) to the point …