TY - GEN

T1 - Open problem

T2 - 26th Conference on Learning Theory, COLT 2013

AU - Koren, Tomer

PY - 2013

Y1 - 2013

N2 - Stochastic exp-concave optimization is an important primitive in machine learning that captures several fundamental problems, including linear regression, logistic regression and more. The exp-concavity property allows for fast convergence rates, as compared to general stochastic optimization. However, current algorithms that attain such rates scale poorly with the dimension n and run in time O(n4), even on very simple instances of the problem. The question we pose is whether it is possible to obtain fast rates for exp-concave functions using more computationally-efficient algorithms.

AB - Stochastic exp-concave optimization is an important primitive in machine learning that captures several fundamental problems, including linear regression, logistic regression and more. The exp-concavity property allows for fast convergence rates, as compared to general stochastic optimization. However, current algorithms that attain such rates scale poorly with the dimension n and run in time O(n4), even on very simple instances of the problem. The question we pose is whether it is possible to obtain fast rates for exp-concave functions using more computationally-efficient algorithms.

UR - http://www.scopus.com/inward/record.url?scp=84898037492&partnerID=8YFLogxK

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AN - SCOPUS:84898037492

T3 - Proceedings of Machine Learning Research

SP - 1073

EP - 1075

BT - Proceedings of the 26th Annual Conference on Learning Theory

A2 - Shalev-Shwartz, Shai

A2 - Steinwart, Ingo

PB - PMLR

Y2 - 12 June 2013 through 14 June 2013

ER -