ICML 2019oral23 citations
Functional Transparency for Structured Data: a Game-Theoretic Approach
Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi Jaakkola
Abstract
We provide a new approach to training neural models to exhibit transparency in a well-defined, functional manner. Our approach naturally operates over structured data and tailors the predictor, functionally, towards a chosen family of (local) witnesses. The estimation problem is setup as a co-operative game between an unrestricted
BibTeX
@InProceedings{pmlr-v97-lee19b,
title = {Functional Transparency for Structured Data: a Game-Theoretic Approach},
author = {Lee, Guang-He and Jin, Wengong and Alvarez-Melis, David and Jaakkola, Tommi},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {3723--3733},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/lee19b/lee19b.pdf},
url = {https://proceedings.mlr.press/v97/lee19b.html},
abstract = {We provide a new approach to training neural models to exhibit transparency in a well-defined, functional manner. Our approach naturally operates over structured data and tailors the predictor, functionally, towards a chosen family of (local) witnesses. The estimation problem is setup as a co-operative game between an unrestricted