@report {1616724,
	title = {Least Squares Revisited: Scalable Approaches for Multi-class Prediction},
	year = {2014},
	institution = {ArXiv Report},
	address = {ICML},
	abstract = {This work provides simple algorithms for multi-class (and multi-label) prediction in settings where both the number of examples n and the data dimension d are relatively large. These robust and parameter free algorithms are essentially iterative least-squares updates and very versatile both in theory and in practice. On the theoretical front, we present several variants with convergence guarantees. Owing to their effective use of second-order structure, these algorithms are substantially better than first-order methods in many practical scenarios. On the empirical side, we present a scalable stagewise variant of our approach, which achieves dramatic computational speedups over popular optimization packages such as Liblinear and Vowpal Wabbit on standard datasets (MNIST and CIFAR-10), while attaining state-of-the-art accuracies.},
	url = {http://arxiv.org/abs/1310.1949},
	author = {Alekh Agarwal and Sham M. Kakade and Nikos Karampatziakis and Le Song and Gregory Valiant}
}
