Calibration-then-Calculation: A Variance Reduced Metric Framework

Yewen Fan, Nian Si, Xiangchen Song, Kun Zhang

Primary Area: general machine learning (i.e., none of the above)

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Keywords: Recommendation System, Deep Learning, Calibration, Metric, Deep Click-Through Rate Prediction Models, Neural Networks

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2024/AuthorGuide.

TL;DR: propose a new metric framework to evaluate deep learning pipelines more accurately

Abstract: Deep learning has been widely adopted across various fields, but there has been little focus on evaluating the performance of deep learning pipelines. With the increased use of large datasets and complex models, it has become common to run the training process only once and compare the result to previous benchmarks. However, this procedure can lead to imprecise comparisons due to the variance in neural network evaluation metrics. The metric variance comes from the randomness inherent in the training process of deep learning pipelines. Traditional solutions such as running the training process multiple times are usually not feasible in deep learning due to computational limitations. In this paper, we propose a new metric framework, Calibrated Loss, that addresses this issue by reducing the variance in its vanilla counterpart. As a result, the new metric has a higher accuracy to detect effective modeling improvement. Our approach is supported by theoretical justifications and extensive experimental validations in the context of Deep Click-Through Rate Prediction Models and Image Classification Models.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 7845