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stat.ML2022★ 4 cited
Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning
Milad Sefidgaran, Romain Chor, Abdellatif Zaidi
In this paper, we use tools from rate-distortion theory to establish new upper bounds on the generalization error of statistical distributed learning algorithms. Specifically, ther…
stat.ML2022★ 6 cited
Rate-Distortion Theoretic Generalization Bounds for Stochastic Learning Algorithms
Milad Sefidgaran, Amin Gohari, Gaël Richard +1
Understanding generalization in modern machine learning settings has been one of the major challenges in statistical learning theory. In this context, recent years have witnessed t…