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 3DP3, a framework for inverse graphics that uses inference in a structured
generative model of objects, scenes, and images. 3DP3 uses (i) voxel models to represent …

People routinely infer the goals of others by observing their actions over time. Remarkably,
we can do so even when those actions lead to failure, enabling us to assist others when we …

Optimizing the expected values of probabilistic processes is a central problem in computer
science and its applications, arising in fields ranging from artificial intelligence to operations …

Abstract Markov Chain Monte Carlo (MCMC) is a computational approach to fundamental
problems such as inference, integration, optimization, and simulation. The field has …

This paper introduces SMCP3, a method for automatically implementing custom sequential
Monte Carlo samplers for inference in probabilistic programs. Unlike particle filters and …

Since its inception the Metropolis-Hastings kernel has been applied in sophisticated ways to
address ever more challenging and diverse sampling problems. Its success stems from the …

The posterior in probabilistic programs with stochastic support decomposes as a weighted
sum of the local posterior distributions associated with each possible program path. We …

Abstract Hamiltonian Monte Carlo (HMC) is a powerful algorithm to sample latent variables
from Bayesian models. The advent of probabilistic programming languages (PPLs) frees …

Integrated task and motion planning (TAMP) has proven to be a valuable approach to
generalizable long-horizon robotic manipulation and navigation problems. However, the …