Estimating relatedness via data compression

We show that it is possible to use data compression on independently obtained hypotheses from various tasks to algorithmically provide guarantees that the tasks are sufficiently related to benefit from multitask learning. We give uniform bounds in terms of the empirical average error for the true average error of the n hypotheses provided by deterministic learning algorithms drawing independent samples from a set of n unknown computable task distributions over finite sets.

• Type: Conference
• Year: 2006
• Where: ICML
• Authors: Brendan Juba
• Keywords: Computable Task Distributions, Empirical Average Error, ICML 2006, Machine Learning, True Average Error