ICML 2019oral163 citations
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Chao Ma, Sebastian Tschiatschek, Konstantina Palla, Jose Miguel Hernandez-Lobato, Sebastian Nowozin, Cheng Zhang
Abstract
Many real-life decision making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment. Acquiring more relevant information enables better decision making, but may be costly. How can we trade off the desire to make good decisions by acquiring further information with the cost of performing that acquisition? To this end, we propose a principled framework, named
BibTeX
@InProceedings{pmlr-v97-ma19c,
title = {{EDDI}: Efficient Dynamic Discovery of High-Value Information with Partial {VAE}},
author = {Ma, Chao and Tschiatschek, Sebastian and Palla, Konstantina and Hernandez-Lobato, Jose Miguel and Nowozin, Sebastian and Zhang, Cheng},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {4234--4243},
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/ma19c/ma19c.pdf},
url = {https://proceedings.mlr.press/v97/ma19c.html},
abstract = {Many real-life decision making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment. Acquiring more relevant information enables better decision making, but may be costly. How can we trade off the desire to make good decisions by acquiring further information with the cost of performing that acquisition? To this end, we propose a principled framework, named