6 citations · 7 across the 2 of their papers we have counts for
4 papers
Random Reshuffling with Variance Reduction: New Analysis and Better Rates
Grigory Malinovsky, Alibek Sailanbayev, Peter Richtárik
Virtually all state-of-the-art methods for training supervised machine learning models are variants of SGD enhanced with a number of additional tricks, such as minibatching, moment…
MISO is Making a Comeback With Better Proofs and Rates
Xun Qian, Alibek Sailanbayev, Konstantin Mishchenko +1
MISO, also known as Finito, was one of the first stochastic variance reduced methods discovered, yet its popularity is fairly low. Its initial analysis was significantly limited by…
SGD: General Analysis and Improved Rates
Robert Mansel Gower, Nicolas Loizou, Xun Qian +3
We propose a general yet simple theorem describing the convergence of SGD under the arbitrary sampling paradigm. Our theorem describes the convergence of an infinite array of varia…
Improving SAGA via a Probabilistic Interpolation with Gradient Descent
Adel Bibi, Alibek Sailanbayev, Bernard Ghanem +2
We develop and analyze a new algorithm for empirical risk minimization, which is the key paradigm for training supervised machine learning models. Our method---SAGD---is based on a…