Quantifying Distribution Shifts and Uncertainties for Enhanced Model Robustness in Machine Learning Applications

TL;DR

This paper investigates how to quantify distribution shifts and uncertainties in machine learning models using synthetic data and statistical measures, aiming to improve model robustness and generalization in real-world applications.

Contribution

It introduces a systematic approach combining synthetic data generation and statistical metrics to assess distributional disparities and model uncertainty.

Findings

Mahalanobis distance effectively identifies low-error vs. high-error regimes.

Statistical measures can predict model performance under distribution shifts.

Quantifying uncertainty aids in deploying more robust machine learning models.

Abstract

Distribution shifts, where statistical properties differ between training and test datasets, present a significant challenge in real-world machine learning applications where they directly impact model generalization and robustness. In this study, we explore model adaptation and generalization by utilizing synthetic data to systematically address distributional disparities. Our investigation aims to identify the prerequisites for successful model adaptation across diverse data distributions, while quantifying the associated uncertainties. Specifically, we generate synthetic data using the Van der Waals equation for gases and employ quantitative measures such as Kullback-Leibler divergence, Jensen-Shannon distance, and Mahalanobis distance to assess data similarity. These metrics en able us to evaluate both model accuracy and quantify the associated uncertainty in predictions arising from data distribution shifts. Our findings suggest that utilizing statistical measures, such as the Mahalanobis distance, to determine whether model predictions fall within the low-error "interpolation regime" or the high-error "extrapolation regime" provides a complementary method for assessing distribution shift and model uncertainty. These insights hold significant value for enhancing model robustness and generalization, essential for the successful deployment of machine learning applications in real-world scenarios.