A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as co-variate shift. We also explore some potential future issues in transfer learning research.

Standard inductive learning requires that training and test instances come from the same distribution. Transfer learning seeks to remove this restriction. In shallow transfer, test instances are from the same domain, but have a different distribution. In deep transfer, test instances are from a different domain entirely (i.e., described by different predicates). Humans routinely perform deep transfer, but few learning systems, if any, are capable of it. In this paper we propose an approach based on a form of second-order Markov logic. Our algorithm discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables, and instantiates these formulas with predicates from the target domain. Using this approach, we have successfully transferred learned knowledge among molecular biology, social network and Web domains. The discovered patterns include broadly useful properties of predicates, like symmetry and transitivity, and relations among predicates, such as various forms of homophily. 1.

In activity recognition, one major challenge is huge manual effort in labeling when a new domain of activities is to be tested. In this paper, we ask an interesting question: can we transfer the available labeled data from a set of existing activities in one domain to help recognize the activities in another different but related domain? Our answer is “yes”, provided that the sensor data from the two domains are related in some way. We develop a bridge between the activities in two domains by learning a similarity function via Web search, under the condition that the sensor data are from the same feature space. Based on the learned similarity measures, our algorithm interprets the data from the source domain as the data in the domain with different confidence levels, thus accomplishing the cross-domain knowledge transfer task. Our algorithm is evaluated on several real-world datasets to demonstrate its effectiveness.

We propose the use of statistical relational learning, and in particular the formalism of Markov Logic Networks, for transfer in reinforcement learning. Our goal is to extract relational knowledge from a source task and use it to speed up learning in a related target task. We do so by learning a Markov Logic Network that describes the source-task Q-function, and then using it for decision making in the early learning stages of the target task. Through experiments in the RoboCup simulated-soccer domain, we show that this approach can provide a substantial performance benefit in the target task.

The field of inductive logic programming (ILP) has made steady progress, since the first ILP workshop in 1991, based on a balance of developments in theory, implementations and applications. More recently there has been an increased emphasis on Probabilistic ILP and the related fields of Statistical Relational Learning (SRL) and Structured Prediction. The goal of the current paper is to consider these emerging trends and chart out the strategic directions and open problems for the broader area of structured machine learning for the next 10 years.

Abstract. Transfer learning is the improvement of learning in a new task through the transfer of knowledge from a related task that has already been learned. While most machine learning algorithms are designed to address single tasks, the development of algorithms that facilitate transfer learning is a topic of ongoing interest in the machine-learning community. This chapter provides an introduction to the goals, formulations, and challenges of transfer learning. It surveys current research in this area, giving an overview of the state of the art and outlining the open problems. The survey covers transfer in both inductive learning and reinforcement learning, and discusses the issues of negative transfer and task mapping in depth.

Activities of daily living are good indicators of the health status of elderly. Therefore, automating the monitoring of these activities is a crucial step in future care giving. However, many models for activity recognition rely on labeled examples of activities for learning the model parameters. Due to the high variability of different contexts, parameters learned for one context can not automatically be used in another. In this paper, we present a method that allows us to transfer knowledge of activity recognition from one context to the next, a task called transfer learning. We show the effectiveness of our method using real world datasets.

A central goal of transfer learning is to enable learning when training data from the domain of interest is limited. Yet, work on transfer across relational domains has so far focused on the case where there is a significant amount of target data. This paper bridges this gap by studying transfer when the amount of target data is minimal and consists of information about just a handful of entities. In the extreme case, only a single entity is known. We present the SR2LR algorithm that finds an effective mapping of predicates from a source model to the target domain in this setting and thus renders preexisting knowledge useful to the target task. We demonstrate SR2LR’s effectiveness in three benchmark relational domains on social interactions and study its behavior as information about an increasing number of entities becomes available.

Abstract. Learning action models is an important and difficult task for AI planning, since it is both time-consuming and tedious for a human to encode the action models by hand using a formal language such as PDDL. In this paper, we present a new algorithm to learn action models from plan traces by transferring useful knowledge from another domain whose action models are already known. We call this algorithm t-LAMP, (transfer Learning Action Models from Plan traces) which can learn action models in PDDL language with quantifiers from plan traces where the intermediate states can contain noise and partial information. We apply Markov Logic Network to enable knowledge transfer, and show that using the transfer learning framework, the quality of the learned action models are generally better than the case when not using an existing domain for transfer. 1

Thanks to my parents for the Commodore 64 (among many other things), to my advisor for getting me interested in this line of work and supporting me along the way, and to all of the other researchers I interacted with, especially the organizers of and participants in the Trading Agent Competition.