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A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method
Simon Lacoste-Julien, Mark Schmidt, Francis Bach
In this note, we present a new averaging technique for the projected stochastic subgradient method. By using a weighted average with a weight of t+1 for each iterate w_t at iterati…
SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases
Simon Lacoste-Julien, Konstantina Palla, Alex Davies +3
The Internet has enabled the creation of a growing number of large-scale knowledge bases in a variety of domains containing complementary information. Tools for automatically align…
On the Equivalence between Herding and Conditional Gradient Algorithms
Francis Bach, Simon Lacoste-Julien, Guillaume Obozinski
We show that the herding procedure of Welling (2009) takes exactly the form of a standard convex optimization algorithm--namely a conditional gradient algorithm minimizing a quadra…