22 citations · 30 across the 5 of their papers we have counts for
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cs.LG2020
Revisiting Projection-free Online Learning: the Strongly Convex Case
Dan Garber, Ben Kretzu
Projection-free optimization algorithms, which are mostly based on the classical Frank-Wolfe method, have gained significant interest in the machine learning community in recent ye…
math.OC2020
Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity
Dan Garber
In recent years it was proved that simple modifications of the classical Frank-Wolfe algorithm (aka conditional gradient algorithm) for smooth convex minimization over convex and c…
cs.LG2020
On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems
Dan Garber
We revisit the use of Stochastic Gradient Descent (SGD) for solving convex optimization problems that serve as highly popular convex relaxations for many important low-rank matrix…