ICML 2022spotlight0 citations
Centroid Approximation for Bootstrap: Improving Particle Quality at Inference
Abstract
Bootstrap is a principled and powerful frequentist statistical tool for uncertainty quantification. Unfortunately, standard bootstrap methods are computationally intensive due to the need of drawing a large i.i.d. bootstrap sample to approximate the ideal bootstrap distribution; this largely hinders their application in large-scale machine learning, especially deep learning problems. In this work, we propose an efficient method to explicitly
BibTeX
@InProceedings{pmlr-v162-ye22a,
title = {Centroid Approximation for Bootstrap: Improving Particle Quality at Inference},
author = {Ye, Mao and Liu, Qiang},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {25469--25489},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/ye22a/ye22a.pdf},
url = {https://proceedings.mlr.press/v162/ye22a.html},
abstract = {Bootstrap is a principled and powerful frequentist statistical tool for uncertainty quantification. Unfortunately, standard bootstrap methods are computationally intensive due to the need of drawing a large i.i.d. bootstrap sample to approximate the ideal bootstrap distribution; this largely hinders their application in large-scale machine learning, especially deep learning problems. In this work, we propose an efficient method to explicitly