44 citations · 543 across the 47 of their papers we have counts for
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Generalization Bounds for Stochastic Gradient Descent via Localized -Covers
Sejun Park, Umut Şimşekli, Murat A. Erdogdu
In this paper, we propose a new covering technique localized for the trajectories of SGD. This localization provides an algorithm-specific complexity measured by the covering numbe…
Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares
Anant Raj, Melih Barsbey, Mert Gürbüzbalaban +2
Recent studies have shown that heavy tails can emerge in stochastic optimization and that the heaviness of the tails have links to the generalization error. While these studies hav…
Heavy-Tail Phenomenon in Decentralized SGD
Mert Gurbuzbalaban, Yuanhan Hu, Umut Simsekli +2
Recent theoretical studies have shown that heavy-tails can emerge in stochastic optimization due to `multiplicative noise', even under surprisingly simple settings, such as linear…
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…