Domain adaptation algorithms seek to generalize a model trained in a source domain to a new target domain. In many practical cases, the source and target distributions can differ substantially, and in some cases crucial target features may not have support in the source domain. In this paper we introduce an algorithm that bridges the gap between source and target domains by slowly adding to the training set both the target features and instances in which the current algorithm is the most confident. Our algorithm is a variant of co-training [7], and we name it CODA (Co-training for domain adaptation). Unlike the original co-training work, we do not assume a particular feature split. Instead, for each iteration of cotraining, we formulate a single optimization problem which simultaneously learns a target predictor, a split of the feature space into views, and a subset of source and target features to include in the predictor. CODA significantly out-performs the state-of-the-art on the 12-domain benchmark data set of Blitzer et al. [4]. Indeed, over a wide range (65 of 84 comparisons) of target supervision CODA achieves the best performance. 1

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. 1