95 citations · 203 across the 21 of their papers we have counts for
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Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings
Jan Macdonald, Mathieu Besançon, Sebastian Pokutta
We study the effects of constrained optimization formulations and Frank-Wolfe algorithms for obtaining interpretable neural network predictions. Reformulating the Rate-Distortion E…
Scalable Frank-Wolfe on Generalized Self-concordant Functions via Simple Steps
Alejandro Carderera, Mathieu Besançon, Sebastian Pokutta
Generalized self-concordance is a key property present in the objective function of many important learning problems. We establish the convergence rate of a simple Frank-Wolfe vari…
FrankWolfe.jl: a high-performance and flexible toolbox for Frank-Wolfe algorithms and Conditional Gradients
Mathieu Besançon, Alejandro Carderera, Sebastian Pokutta
We present FrankWolfe.jl, an open-source implementation of several popular Frank-Wolfe and Conditional Gradients variants for first-order constrained optimization. The package is d…