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Asynchronous parallel stochastic optimization for non-convex problems becomes more and more important in machine learning especially due to the popularity of deep learning. The Frank-Wolfe (a.k.a. conditional gradient) algorithms has regained much interest because of its projection-free property and the ability of handling structured constraints. However, our understanding of asynchronous stochastic Frank-Wolfe algorithms is extremely limited especially in the non-convex setting. To addressdoi:10.24963/ijcai.2019/104 dblp:conf/ijcai/GuXH19 fatcat:y2fccqpyqzhnfc2hqqdelh3fte