The Tradeoffs of Large Scale Learning

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

Bibtex »Metadata »Paper »

Authors

Léon Bottou, Olivier Bousquet

Abstract

<p>This contribution develops a theoretical framework that takes into account the effect of approximate optimization on learning algorithms. The analysis shows distinct tradeoffs for the case of small-scale and large-scale learning problems. Small-scale learning problems are subject to the usual approximation--estimation tradeoff. Large-scale learning problems are subject to a qualitatively different tradeoff involving the computational complexity of the underlying optimization algorithms in non-trivial ways.</p>