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One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods
[article]
2020
arXiv
pre-print
We propose a remarkably general variance-reduced method suitable for solving regularized empirical risk minimization problems with either a large number of training examples, or a large model dimension, or both. In special cases, our method reduces to several known and previously thought to be unrelated methods, such as SAGA, LSVRG, JacSketch, SEGA and ISEGA, and their arbitrary sampling and proximal generalizations. However, we also highlight a large number of new specific algorithms with
arXiv:1905.11266v2
fatcat:hnhbmvpsljf3rkt43g7j3gu4dy