NeurIPS 2017poster20 citations
The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities
Arun Suggala, Mladen Kolar, Pradeep K Ravikumar
Abstract
Non-parametric multivariate density estimation faces strong statistical and computational bottlenecks, and the more practical approaches impose near-parametric assumptions on the form of the density functions. In this paper, we leverage recent developments to propose a class of non-parametric models which have very attractive computational and statistical properties. Our approach relies on the simple function space assumption that the conditional distribution of each variable conditioned on the other variables has a non-parametric exponential family form.
BibTeX
@inproceedings{NIPS2017_fd69dbe2,
author = {Suggala, Arun and Kolar, Mladen and Ravikumar, Pradeep K},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities},
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/fd69dbe29f156a7ef876a40a94f65599-Paper.pdf},
volume = {30},
year = {2017}
}