Even after fine-tuning and reinforcement learning, large language models (LLMs) can be
difficult, if not impossible, to control reliably with prompts alone. We propose a new inference …

This paper presents a new approach to automatically discovering accurate models of
complex time series data. Working within a Bayesian nonparametric prior over a symbolic …

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

Compared to the wide array of advanced Monte Carlo methods supported by modern
probabilistic programming languages (PPLs), PPL support for variational inference (VI) is …

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 …

Motivated by the maneuvering target tracking with sensors such as radar and sonar, this
article considers the joint and recursive estimation of the dynamic state and the time-varying …

The space of human goals is tremendously vast; and yet, from just a few moments of
watching a scene or reading a story, we seem to spontaneously infer a range of plausible …

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 …

Abstract Reasoning about causal relationships is central to the human experience. This
evokes a natural question in our pursuit of human-like artificial intelligence: how might we …

Probabilistic programming has emerged as a powerful framework for building expressive
models that can handle uncertainty in a wide range of applications. Serialization, the …