What do we want from machine intelligence? We envision machines that are not just tools
for thought but partners in thought: reasonable, insightful, knowledgeable, reliable and …

How does language inform our downstream thinking? In particular, how do humans make
meaning from language--and how can we leverage a theory of linguistic meaning to build …

We present a novel static analysis technique to derive higher moments for program
variables for a large class of probabilistic loops with potentially uncountable state spaces …

We present an exact Bayesian inference method for discrete statistical models, which can
find exact solutions to a large class of discrete inference problems, even with infinite support …

Distributions on integers are ubiquitous in probabilistic modeling but remain challenging for
many of today's probabilistic programming languages (PPLs). The core challenge comes …

Recursive calls over recursive data are useful for generating probability distributions, and
probabilistic programming allows computations over these distributions to be expressed in a …

A streaming probabilistic program receives a stream of observations and produces a stream
of distributions that are conditioned on these observations. Efficient inference is often …

We propose a new method to approximate the posterior distribution of probabilistic
programs by means of computing guaranteed bounds. The starting point of our work is an …

We present a new approach to the design and implementation of probabilistic programming
languages (PPLs), based on the idea of stochastically estimating the probability density …

We study the problem of bounding the posterior distribution of discrete probabilistic
programs with unbounded support, loops, and conditioning. Loops pose the main difficulty in …