Statistics Papers

Document Type

Working Paper

Date of this Version

2011

Publication Source

Proceedings of the 14th International Conference on Artificial Intelligence and Statistics

Volume

15

Start Page

173

Last Page

181

Abstract

Domain adaptation algorithms address a key issue in applied machine learning: How can we train a system under a source distribution but achieve high performance under a different target distribution? We tackle this question for divergent distributions where crucial predictive target features may not even have support under the source distribution. In this setting, the key intuition is that that if we can link target-specific features to source features, we can learn effectively using only source labeled data. We formalize this intuition, as well as the assumptions under which such coupled learning is possible. This allows us to give finite sample target error bounds (using only source training data) and an algorithm which performs at the state-of-the-art on two natural language processing adaptation tasks which are characterized by novel target features.

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Date Posted: 27 November 2017

This document has been peer reviewed.