Many statistical learning models hold an assumption that the training data and the future unlabeled data are drawn from the same distribution. However, this assumption is difficult to fulfill in real-world scenarios and creates barriers in reusing existing labels from similar application domains. Transfer Learning is intended to relax this assumption by modeling relationships between domains, and is often applied in deep learning applications to reduce the demand for labeled data and training time. Despite recent advances in exploring deep learning models with visual analytics tools, little work has explored the issue of explaining and diagnosing the knowledge transfer process between deep learning models. In this paper, we present a visual analytics framework for the multi-level exploration of the transfer learning processes when training deep neural networks. Our framework establishes a multi-aspect design to explain how the learned knowledge from the existing model is transferred into the new learning task when training deep neural networks. Based on a comprehensive requirement and task analysis, we employ descriptive visualization with performance measures and detailed inspections of model behaviors from the statistical, instance, feature, and model structure levels. We demonstrate our framework through two case studies on image classification by fine-tuning AlexNets to illustrate how analysts can utilize our framework.

The need for annotated labels to train machine learning models led to a surge in crowdsourcing - collecting labels from non-experts. Instead of annotating from scratch, given an imperfect labeled set, how can we leverage the label information obtained from amateur crowd workers to improve the data quality? Furthermore, is there a way to teach the amateur crowd workers using this imperfect labeled set in order to improve their labeling performance? In this paper, we aim to answer both questions via a novel interactive teaching framework, which uses visual explanations to simultaneously teach and gauge the confidence level of the crowd workers.

With the advancements in many data mining and machine learning tasks, together with the availability of large-scale annotated data sets, there have been an increasing number of off-the-shelf tools for addressing these tasks, like Stanford NLP Toolkit and Caffe Model Zoo. However, many of these tasks are time-evolving in nature due to, e.g., the emergence of new features and the change of class conditional distribution of features. As a result, the off-the-shelf tools are not able to adapt to such changes and will suffer from sub-optimal performance in the target application. In this paper, we propose a generic framework named AOT for adapting the outputs from an off-the-shelf tool to accommodate the changes in the learning task. It considers two major types of changes, i.e., label deficiency and distribution shift, and aims to maximally boost the performance of the off-the-shelf tool in the target domain, with the help of a limited number of target domain labeled examples. Furthermore, we propose an iterative algorithm to solve the resulting optimization problem, and we demonstrate the superior performance of the proposed AOT framework on text and image data sets.

With the increasing demand for large amount of labeled data, crowdsourcing has been used in many large-scale data mining applications. However, most existing works in crowdsourcing mainly focus on label inference and incentive design. In this paper, we address a different problem of adaptive crowd teaching, which is a sub-area of machine teaching in the context of crowdsourcing. Compared with machines, human beings are extremely good at learning a specific target concept (e.g., classifying the images into given categories) and they can also easily transfer the learned concepts into similar learning tasks. Therefore, a more effective way of utilizing crowdsourcing is by supervising the crowd to label in the form of teaching. In order to perform the teaching and expertise estimation simultaneously, we propose an adaptive teaching framework named JEDI to construct the personalized optimal teaching set for the crowdsourcing workers. In JEDI teaching, the teacher assumes that each learner has an exponentially decayed memory. Furthermore, it ensures comprehensiveness in the learning process by carefully balancing teaching diversity and learner's accurate learning in terms of teaching usefulness. Finally, we validate the effectiveness and efficacy of JEDI teaching in comparison with the state-of-the-art techniques on multiple data sets with both synthetic learners and real crowdsourcing workers.

With the emergence of online forums associated with major diseases, such as diabetes mellitus, many patients are increasingly dependent on such disease-specific social networks to gain access to additional resources. Among these patients, it is common for them to stick to one disease-specific social network, although their desired resources might be spread over multiple social networks, such as patients with similar questions and concerns. Motivated by this application, in this paper, we focus on cross network link recommendation, which aims to identify similar users across multiple heterogeneous social networks. The problem setting is different from existing work on cross network link prediction, which either tries to link accounts of the same user from different social networks, or aims to match users with complementary expertise or interest. To approach the problem of cross network link recommendation, we propose to jointly decompose the user-keyword matrices from multiple social networks, while requiring them to share the same topics and user group-topic association matrices. This constraint comes from the fact that social networks dedicated to the same disease tend to share the same topics as well as the interests of users groups in certain topics. Based on this intuition, we construct a generic optimization framework, provide four instantiations and an iterative optimization algorithm with performance analysis. In the experiments, we demonstrate the superiority of the proposed algorithm over state-of-the-art techniques on various real-world data sets.

Sentiment analysis has been studied for decades, and it is widely used in many real applications such as media monitoring. In sentiment analysis, when addressing the problem of limited labeled data from the target domain, transfer learning, or domain adaptation, has been successfully applied, which borrows information from a relevant source domain with abundant labeled data to improve the prediction performance in the target domain. The key to transfer learning is how to model the relatedness among different domains. For sentiment analysis, a common practice is to assume similar sentiment polarity for the common keywords shared by different domains. However, existing methods largely overlooked the human factor, i.e., the users who expressed such sentiment. In this paper, we address this problem by explicitly modeling the human factor related to sentiment classification. In particular, we assume that the content generated by the same user across different domains is biased in the same way in terms of the sentiment polarity. In other words, optimistic/pessimistic users demonstrate consistent sentiment patterns, no matter what the context is. To this end, we propose a new graph-based approach named U-Cross, which models the relatedness of different domains via both the shared users and keywords. It is non-parametric and semi-supervised in nature. Furthermore, we also study the problem of shared user selection to prevent ‘negative transfer’. In the experiments, we demonstrate the effectiveness of U-Cross by comparing it with existing state-of-the-art techniques on three real data sets.