NeurIPS 2019poster16 citations
Gradient Information for Representation and Modeling
Jie Ding, Robert Calderbank, Vahid Tarokh
Abstract
Motivated by Fisher divergence, in this paper we present a new set of information quantities which we refer to as gradient information. These measures serve as surrogates for classical information measures such as those based on logarithmic loss, Kullback-Leibler divergence, directed Shannon information, etc. in many data-processing scenarios of interest, and often provide significant computational advantage, improved stability and robustness. As an example, we apply these measures to the Chow-Liu tree algorithm, and demonstrate remarkable performance and significant computational reduction using both synthetic and real data.
BibTeX
@inproceedings{NEURIPS2019_6d9c547c,
author = {Ding, Jie and Calderbank, Robert and Tarokh, Vahid},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Gradient Information for Representation and Modeling},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/6d9c547cf146054a5a720606a7694467-Paper.pdf},
volume = {32},
year = {2019}
}