NeurIPS 2020oral4 citations
Worst-Case Analysis for Randomly Collected Data
Justin Chen, Gregory Valiant, Paul Valiant
Abstract
We introduce a framework for statistical estimation that leverages knowledge of how samples are collected but makes no distributional assumptions on the data values. Specifically, we consider a population of elements [n]={1,...,n} with corresponding data values x
BibTeX
@inproceedings{NEURIPS2020_d34a281a,
author = {Chen, Justin and Valiant, Gregory and Valiant, Paul},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {18183--18193},
publisher = {Curran Associates, Inc.},
title = {Worst-Case Analysis for Randomly Collected Data},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/d34a281acc62c6bec66425f0ad6dd645-Paper.pdf},
volume = {33},
year = {2020}
}