@inproceedings{dodge-etal-2021-expected-validation,
title = "Expected Validation Performance and Estimation of a Random Variable`s Maximum",
author = "Dodge, Jesse and
Gururangan, Suchin and
Card, Dallas and
Schwartz, Roy and
Smith, Noah A.",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.342/",
doi = "10.18653/v1/2021.findings-emnlp.342",
pages = "4066--4073",
abstract = "Research in NLP is often supported by experimental results, and improved reporting of such results can lead to better understanding and more reproducible science. In this paper we analyze three statistical estimators for expected validation performance, a tool used for reporting performance (e.g., accuracy) as a function of computational budget (e.g., number of hyperparameter tuning experiments). Where previous work analyzing such estimators focused on the bias, we also examine the variance and mean squared error (MSE). In both synthetic and realistic scenarios, we evaluate three estimators and find the unbiased estimator has the highest variance, and the estimator with the smallest variance has the largest bias; the estimator with the smallest MSE strikes a balance between bias and variance, displaying a classic bias-variance tradeoff. We use expected validation performance to compare between different models, and analyze how frequently each estimator leads to drawing incorrect conclusions about which of two models performs best. We find that the two biased estimators lead to the fewest incorrect conclusions, which hints at the importance of minimizing variance and MSE."
}
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%0 Conference Proceedings
%T Expected Validation Performance and Estimation of a Random Variable‘s Maximum
%A Dodge, Jesse
%A Gururangan, Suchin
%A Card, Dallas
%A Schwartz, Roy
%A Smith, Noah A.
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F dodge-etal-2021-expected-validation
%X Research in NLP is often supported by experimental results, and improved reporting of such results can lead to better understanding and more reproducible science. In this paper we analyze three statistical estimators for expected validation performance, a tool used for reporting performance (e.g., accuracy) as a function of computational budget (e.g., number of hyperparameter tuning experiments). Where previous work analyzing such estimators focused on the bias, we also examine the variance and mean squared error (MSE). In both synthetic and realistic scenarios, we evaluate three estimators and find the unbiased estimator has the highest variance, and the estimator with the smallest variance has the largest bias; the estimator with the smallest MSE strikes a balance between bias and variance, displaying a classic bias-variance tradeoff. We use expected validation performance to compare between different models, and analyze how frequently each estimator leads to drawing incorrect conclusions about which of two models performs best. We find that the two biased estimators lead to the fewest incorrect conclusions, which hints at the importance of minimizing variance and MSE.
%R 10.18653/v1/2021.findings-emnlp.342
%U https://aclanthology.org/2021.findings-emnlp.342/
%U https://doi.org/10.18653/v1/2021.findings-emnlp.342
%P 4066-4073
Markdown (Informal)
[Expected Validation Performance and Estimation of a Random Variable’s Maximum](https://aclanthology.org/2021.findings-emnlp.342/) (Dodge et al., Findings 2021)
ACL