Adviserr: Amin Karbasi
Abstract: As a projection-free algorithm, Frank-Wolfe (FW) method, also known as conditional gradient, has recently received considerable attention in the machine learning community. In this dissertation1, we study several topics on the FW variants for scalable projection-free optimization. We first propose 1-SFW, the first projection-free method that requires only one sample per iteration to update the optimization variable and yet achieves the best known complexity bounds for convex, non-convex, and monotone DR-submodular settings. Then we move forwardtothedistributedsetting,anddevelopQuantized Frank-Wolfe(QFW),a general communication-efficient distributed FW framework for both convex and non-convex objective functions. We study the performance of QFW in two widely recognized settings: 1) stochastic optimization and 2) finite-sum optimization. Finally, we propose Black-Box Continuous Greedy,aderivative-freeandprojection-freealgorithm,thatmaximizes a monotone continuous DR-submodular function over a bounded convex body in Euclidean space.