Generalization and Overfitting

A huge part of the recent success of highly parametrized ML models is due to their apparent ability to generalize to unseen data. This ability is seemingly in tension with mathematical results from traditional statistics (e.g. bias-variance trade-off) and statistical learning theory (e.g. PAC theorems) which rely heavily on either strong assumptions about the underlying probability distribution or restrictions on the hypothesis class. The predominant engineering epistemology claims failure of ML theory and suggests that contemporary ML models generalize well even beyond the classical overfitting regime.

This workshop aims to shed light at the generalization overfit tension. For more information and a schedule please see our website https://philo.hlrs.de/?p=415.