A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares)

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Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression

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Averaged least-mean-squares: Bias-variance trade-offs and optimal sampling distributions

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Non-parametric stochastic approximation with large step sizes

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Competing with the empirical risk minimizer in a single pass

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Parallelizing stochastic approximation through mini-batching and tail-averaging

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Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, and Aaron Sidford · 2016

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