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In optimization for Machine learning (ML), it is typical thatcurvature-matrix (CM) estimates rely on an exponential average (EA) of localestimates (giving EA-CM algorithms). This approach has little principledjustification, but is very often used in practice. In this paper, we draw aconnection between EA-CM algorithms and what we call a "Wake of Quadraticregularized models". The outlined connection allows us to understand what EA-CMalgorithms are doing from an optimization perspective. Generalizing from theestablished connection, we propose a new family of algorithms, "KL-DivergenceWake-Regularized Models" (KLD-WRM). We give three different practicalinstantiations of KLD-WRM, and show numerically that these outperform K-FAC onMNIST.

Rethinking Exponential Averaging of the Fisher