Learning Symmetric Embeddings for Equivariant World Models

1 code implementation • 24 Apr 2022 • Jung Yeon Park, Ondrej Biza, Linfeng Zhao, Jan Willem van de Meent, Robin Walters

Incorporating symmetries can lead to highly data-efficient and generalizable models by defining equivalence classes of data samples related by transformations.

A New Approach to Probabilistic Programming Inference

no code implementations • 3 Jul 2015 • Frank Wood, Jan Willem van de Meent, Vikash Mansinghka

We introduce and demonstrate a new approach to inference in expressive probabilistic programming languages based on particle Markov chain Monte Carlo.

Probabilistic Programming

Path Finding under Uncertainty through Probabilistic Inference

no code implementations • 25 Feb 2015 • David Tolpin, Brooks Paige, Jan Willem van de Meent, Frank Wood

We introduce a new approach to solving path-finding problems under uncertainty by representing them as probabilistic models and applying domain-independent inference algorithms to the models.

Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs

1 code implementation • 22 Jan 2015 • David Tolpin, Jan Willem van de Meent, Brooks Paige, Frank Wood

We introduce an adaptive output-sensitive Metropolis-Hastings algorithm for probabilistic models expressed as programs, Adaptive Lightweight Metropolis-Hastings (AdLMH).