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

Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a
crucial challenge, especially for AI systems with a high degree of autonomy and general …

We present a probabilistic procedural modeling strategy for capturing the distribution of
scenes of varying numbers of rigid objects, motivated by the need for such models to enable …

Having a precise understanding of the distribution over worlds a robot will face is critical to
most problems in robotics. This distribution informs mechanical and software design …