no code implementations • 25 Jul 2023 • William X. Cao, Poorva Garg, Ryan Tjoa, Steven Holtzen, Todd Millstein, Guy Van Den Broeck
Distributions on integers are ubiquitous in probabilistic modeling but remain challenging for many of today's probabilistic programming languages (PPLs).
1 code implementation • 25 May 2023 • Federico Cassano, Ming-Ho Yee, Noah Shinn, Arjun Guha, Steven Holtzen
TypeScript and Python are two programming languages that support optional type annotations, which are useful but tedious to introduce and maintain.
no code implementations • 19 Oct 2021 • Ellie Y. Cheng, Todd Millstein, Guy Van Den Broeck, Steven Holtzen
Many of today's probabilistic programming languages (PPLs) have brittle inference performance: the performance of the underlying inference algorithm is very sensitive to the precise way in which the probabilistic program is written.
no code implementations • 26 Jun 2020 • Honghua Zhang, Steven Holtzen, Guy Van Den Broeck
Central to this effort is the development of tractable probabilistic models (TPMs): models whose structure guarantees efficient probabilistic inference algorithms.
1 code implementation • 18 May 2020 • Steven Holtzen, Guy Van Den Broeck, Todd Millstein
This reduction separates the structure of the distribution from its parameters, enabling logical reasoning tools to exploit that structure for probabilistic inference.
Programming Languages
1 code implementation • 12 Mar 2019 • Steven Holtzen, Todd Millstein, Guy Van Den Broeck
A key goal in the design of probabilistic inference algorithms is identifying and exploiting properties of the distribution that make inference tractable.
no code implementations • ICML 2018 • Steven Holtzen, Guy Broeck, Todd Millstein
Experimentally, we also illustrate the practical benefits of our framework as a tool to decompose probabilistic program inference.
no code implementations • 28 May 2017 • Steven Holtzen, Todd Millstein, Guy Van Den Broeck
Abstraction is a fundamental tool for reasoning about complex systems.