Generalization Bounds for Stochastic Gradient Langevin Dynamics: A Unified View via Information Leakage Analysis
This work addresses the theoretical understanding of generalization in machine learning algorithms, specifically for researchers in optimization and privacy, but it is incremental as it unifies existing frameworks.
The paper tackles the problem of deriving generalization bounds for Stochastic Gradient Langevin Dynamics (SGLD) in non-convex empirical risk minimization by presenting a unified view based on privacy leakage analysis, and it provides theoretical and empirical results that explain prior work on SGLD's membership privacy.
Recently, generalization bounds of the non-convex empirical risk minimization paradigm using Stochastic Gradient Langevin Dynamics (SGLD) have been extensively studied. Several theoretical frameworks have been presented to study this problem from different perspectives, such as information theory and stability. In this paper, we present a unified view from privacy leakage analysis to investigate the generalization bounds of SGLD, along with a theoretical framework for re-deriving previous results in a succinct manner. Aside from theoretical findings, we conduct various numerical studies to empirically assess the information leakage issue of SGLD. Additionally, our theoretical and empirical results provide explanations for prior works that study the membership privacy of SGLD.