Feature Design for Transfer Learning

Discriminative learning methods for classification perform well when training and test data are drawn from the same distribution and labeled using the same function. However, often we have labeled data for a task related to the target task but not for the target task itself. Under what conditions does a good classifier for the related task transfer to the target task? Feature representations that capture uniformities between the two tasks are a crucial factor in the success of transfer. We formalize this intuition theoretically with a generalization bound for transfer, and demonstrate it empirically with a particular kind of representation, user-centric features, that helps transfer an email reply predictor between different email users. 1. Transfer Learning Human beings can naturally transfer knowledge about a sourcetask to perform better on a closely related target task. For example a veteran clerk beginning a job at a new store has experience that allows her to operate at a high level of proficiency from the start. An experienced secretary will easily adapt to scheduling appointments in a computer science department, even if his former job was in the psychology department. This is the basic principle behind transfer learning: a system trained on a source task should achieve good performance on related target tasks faster than untrained systems. Discriminative learning methods for classification are based on the assumption that training and test data are drawn from the same distribution. This assumption underlies both theoretical estimates of generalization error and the many experimental evaluations of learning methods. However, often we have little or no training data for a tarPreliminary work. Appeared in the North East Student Colloquim on Artificial Intelligence. get task, but we have plentiful training data for a related source task. In those situations, we would like to transfer the classifier learned for the source task to the target task. In this work we focus on transfer via a common feature representation for tasks. By designing a good representation, the two tasks are mapped together so that a learner for one task automatically performs well on another, without meta-learning or additional training data for the new task. We give theoretical results showing that representation is crucial to transfer performance. We further show significant improvements on the real-world problem of email reply prediction, where we have only one source task and no training data for the target task. In email reply prediction, the goal of the learner is to predict if an email user will reply to a given message. The task is user-dependent in two ways. First, each user receives a different message stream. Second, each user has his own rule for replying to messages. We transfer a classifier between two users through the design of user-centric (or deictic) features. We call these features user-centric because each task involves a different email recipient, and the features capture relations between messages and recipients, rather than intrinsic properties of the messages. Transfer learning is an important problem in machine learning, and it has been studied in many different areas. Some theoretical analyses of transfer that are closely related to our work are Baxter (1995) , who introduces the process of learning a common representation or “environment” from which discriminative hypotheses may be drawn. BenDavid (2003) described task-relatedness in terms of mappings from a domain of learning X to itself. Crammer et al. (2005) investigate a setting where data drawn from a fixed unknown distribution is partitioned into subsets labeled by similar but not identical concepts. These works assume the existence of several related source tasks, all of which have training data. We do not, and it is important to observe that our method gives significant gains even with a single source task. Sutton and McCallum (2005) also use training data from only one source task, but they assume training data in the target task. One of our Feature Design for Transfer Learning most crucial contributions is that our work is effective with no training data from the target task. The remainder of this paper is structured as follows: Section 2 describes in greater detail our problem setting. Section 3 gives a precise definition of transfer representations and an error bound that demonstrates the importance appropriate representations. Section 4 introduces email reply prediction, our experimental task that requires a transferable model. Sections 5-6 show empirically how usercentric features, designed for transfer among email users, significantly improve this task. We conclude with a discussion of future research directions.