Open Problem: Fast Stochastic Exp-Concave Optimization

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Tomer Koren ;
Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:1073-1075, 2013.

Abstract

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(n^4), 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.

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