The Indian Chefs Process
Abstract
This paper introduces the Indian Chefs Process (ICP), a Bayesian nonparametric prior on the joint space of infinite directed acyclic graphs (DAGs) and orders that generalizes Indian Buffet Processes. As our construction shows, the proposed distribution relies on a latent Beta Process controlling both the orders and outgoing connection probabilities of the nodes, and yields a probability distribution on sparse infinite graphs. The main advantage of the ICP over previously proposed Bayesian nonparametric priors for DAG structures is its greater flexibility. To the best of our knowledge, the ICP is the first Bayesian nonparametric model supporting every possible DAG. We demonstrate the usefulness of the ICP on learning the structure of deep generative sigmoid networks as well as convolutional neural networks.
Cite
@article{arxiv.2001.10657,
title = {The Indian Chefs Process},
author = {Patrick Dallaire and Luca Ambrogioni and Ludovic Trottier and Umut Güçlü and Max Hinne and Philippe Giguère and Brahim Chaib-Draa and Marcel van Gerven and Francois Laviolette},
journal= {arXiv preprint arXiv:2001.10657},
year = {2020}
}