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  • title: Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization
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            Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization
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            Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization

            Jul 12, 2020

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            We propose a novel Stochastic Frank-Wolfe (≡ Conditional Gradient) algorithm with fixed batch size tailored to the constrained optimization of a finite sum of smooth objectives. We make use of a primal-dual interpretation of the Frank-Wolfe algorithm. Recent work to design stochastic variants of the Frank-Wolfe algorithm fall into two categories: algorithms with increasing batch size, and algorithms with constant batch size. The former have faster convergence rates but are impractical; the latt…

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            The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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