- An Information-Theoretic Framework for Out-of-Distribution Generalization(arXiv)
Author : Wenliang Liu, Guanding Yu, Lele Wang, Renjie Liao
Abstract : We analysis the Out-of-Distribution (OOD) generalization in machine learning and recommend a primary framework that provides information-theoretic generalization bounds. Our framework interpolates freely between Integral Chance Metric (IPM) and f-divergence, which naturally recovers some acknowledged outcomes (along with Wasserstein- and KL-bounds), along with yields new generalization bounds. Moreover, we current that our framework admits an optimum transport interpretation. When evaluated in two concrete examples, the proposed bounds each strictly improve upon present bounds in some circumstances or get nicely the perfect amongst present OOD generalization bounds
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