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Variance-Reduced Methods for Machine Learning
Stochastic optimization lies at the heart of machine learning, and its c...
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A new framework for the computation of Hessians
We investigate the computation of Hessian matrices via Automatic Differe...
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Sketched Newton-Raphson
We propose a new globally convergent stochastic second order method. Our...
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Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization
We present a unified theorem for the convergence analysis of stochastic ...
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SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation
We provide several convergence theorems for SGD for two large classes of...
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On the convergence of the Stochastic Heavy Ball Method
We provide a comprehensive analysis of the Stochastic Heavy Ball (SHB) m...
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Factorial Powers for Stochastic Optimization
The convergence rates for convex and non-convex optimization methods dep...
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Fast Linear Convergence of Randomized BFGS
Since the late 1950's when quasi-Newton methods first appeared, they hav...
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Towards closing the gap between the theory and practice of SVRG
Among the very first variance reduced stochastic methods for solving the...
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Optimal mini-batch and step sizes for SAGA
Recently it has been shown that the step sizes of a family of variance r...
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Greedy stochastic algorithms for entropy-regularized optimal transport problems
Optimal transport (OT) distances are finding evermore applications in ma...
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Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization
We present the first accelerated randomized algorithm for solving linear...
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Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods
Our goal is to improve variance reducing stochastic methods through bett...
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