@inproceedings{e21ab03d62b7412bbbd3a888256b7622,
title = "Sparse multi-prototype classification",
abstract = "We introduce a new class of sparse multi-prototype classifiers, designed to combine the computational advantages of sparse predictors with the non-linear power of prototype-based classification techniques. This combination makes sparse multi-prototype models especially well-suited for resource constrained computational platforms, such as the IoT devices. We cast our supervised learning problem as a convex-concave saddle point problem and design a provably-fast algorithm to solve it. We complement our theoretical analysis with an empirical study that demonstrates the merits of our methodology.",
author = "Garg, {Vikas K.} and Lin Xiao and Ofer Dekel",
note = "Publisher Copyright: {\textcopyright} 34th Conference on Uncertainty in Artificial Intelligence 2018. All rights reserved.; 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018 ; Conference date: 06-08-2018 Through 10-08-2018",
year = "2018",
language = "English",
series = "34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018",
publisher = "Association For Uncertainty in Artificial Intelligence (AUAI)",
pages = "704--714",
editor = "Ricardo Silva and Amir Globerson and Amir Globerson",
booktitle = "34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018",
}