5 papers · 1 filter
FANOK: Knockoffs in Linear Time
Armin Askari, Quentin Rebjock, Alexandre d'Aspremont +1
We describe a series of algorithms that efficiently implement Gaussian model-X knockoffs to control the false discovery rate on large scale feature selection problems. Identifying…
Implicit Deep Learning
Laurent El Ghaoui, Fangda Gu, Bertrand Travacca +2
Implicit deep learning prediction rules generalize the recursive rules of feedforward neural networks. Such rules are based on the solution of a fixed-point equation involving a si…
Fenchel Lifted Networks: A Lagrange Relaxation of Neural Network Training
Fangda Gu, Armin Askari, Laurent El Ghaoui
Despite the recent successes of deep neural networks, the corresponding training problem remains highly non-convex and difficult to optimize. Classes of models have been proposed t…
Greedy Frank-Wolfe Algorithm for Exemplar Selection
Gary Cheng, Armin Askari, Kannan Ramchandran +1
In this paper, we consider the problem of selecting representatives from a data set for arbitrary supervised/unsupervised learning tasks. We identify a subset of a data set …
Lifted Neural Networks
Armin Askari, Geoffrey Negiar, Rajiv Sambharya +1
We describe a novel family of models of multi- layer feedforward neural networks in which the activation functions are encoded via penalties in the training problem. Our approach i…