Causally Regularized Learning with Agnostic Data Selection Bias

Most of previous machine learning algorithms are proposed based on the i.i.d. hypothesis. However, this ideal assumption is often violated in real applications, where selection bias may arise between training and testing process. Moreover, in many scenarios, the testing data is not even available during the training process, which makes the traditional methods like transfer learning infeasible due to their need on prior of test distribution. Therefore, how to address the agnostic selection bias for robust model learning is of paramount importance for both academic research and real applications. In this paper, under the assumption that causal relationships among variables are robust across domains, we incorporate causal technique into predictive modeling and propose a novel Causally Regularized Logistic Regression (CRLR) algorithm by jointly optimize global confounder balancing and weighted logistic regression. Global confounder balancing helps to identify causal features, whose causal effect on outcome are stable across domains, then performing logistic regression on those causal features constructs a robust predictive model against the agnostic bias. To validate the effectiveness of our CRLR algorithm, we conduct comprehensive experiments on both synthetic and real world datasets. Experimental results clearly demonstrate that our CRLR algorithm outperforms the state-of-the-art methods, and the interpretability of our method can be fully depicted by the feature visualization.Comment: Oral paper of 2018 ACM Multimedia Conference (MM'18

The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis

In recent years, mobile devices (e.g., smartphones and tablets) have met an increasing commercial success and have become a fundamental element of the everyday life for billions of people all around the world. Mobile devices are used not only for traditional communication activities (e.g., voice calls and messages) but also for more advanced tasks made possible by an enormous amount of multi-purpose applications (e.g., finance, gaming, and shopping). As a result, those devices generate a significant network traffic (a consistent part of the overall Internet traffic). For this reason, the research community has been investigating security and privacy issues that are related to the network traffic generated by mobile devices, which could be analyzed to obtain information useful for a variety of goals (ranging from device security and network optimization, to fine-grained user profiling). In this paper, we review the works that contributed to the state of the art of network traffic analysis targeting mobile devices. In particular, we present a systematic classification of the works in the literature according to three criteria: (i) the goal of the analysis; (ii) the point where the network traffic is captured; and (iii) the targeted mobile platforms. In this survey, we consider points of capturing such as Wi-Fi Access Points, software simulation, and inside real mobile devices or emulators. For the surveyed works, we review and compare analysis techniques, validation methods, and achieved results. We also discuss possible countermeasures, challenges and possible directions for future research on mobile traffic analysis and other emerging domains (e.g., Internet of Things). We believe our survey will be a reference work for researchers and practitioners in this research field.Comment: 55 page

Machine learning for targeted display advertising: Transfer learning in action

This paper presents a detailed discussion of problem formulation and data representation issues in the design, deployment, and operation of a massive-scale machine learning system for targeted display advertising. Notably, the machine learning system itself is deployed and has been in continual use for years, for thousands of advertising campaigns (in contrast to simply having the models from the system be deployed). In this application, acquiring sufficient data for training from the ideal sampling distribution is prohibitively expensive. Instead, data are drawn from surrogate domains and learning tasks, and then transferred to the target task. We present the design of this multistage transfer learning system, highlighting the problem formulation aspects. We then present a detailed experimental evaluation, showing that the different transfer stages indeed each add value. We next present production results across a variety of advertising clients from a variety of industries, illustrating the performance of the system in use. We close the paper with a collection of lessons learned from the work over half a decade on this complex, deployed, and broadly used machine learning system.Statistics Working Papers Serie

Using contextual information to understand searching and browsing behavior

There is great imbalance in the richness of information on the web and the succinctness and poverty of search requests of web users, making their queries only a partial description of the underlying complex information needs. Finding ways to better leverage contextual information and make search context-aware holds the promise to dramatically improve the search experience of users. We conducted a series of studies to discover, model and utilize contextual information in order to understand and improve users' searching and browsing behavior on the web. Our results capture important aspects of context under the realistic conditions of different online search services, aiming to ensure that our scientific insights and solutions transfer to the operational settings of real world applications

Sensing Subjective Well-being from Social Media

Subjective Well-being(SWB), which refers to how people experience the quality of their lives, is of great use to public policy-makers as well as economic, sociological research, etc. Traditionally, the measurement of SWB relies on time-consuming and costly self-report questionnaires. Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated data on social media. By utilizing 1785 users' social media data with SWB labels, we train machine learning models that are able to "sense" individual SWB from users' social media. Our model, which attains the state-by-art prediction accuracy, can then be used to identify SWB of large population of social media users in time with very low cost.Comment: 12 pages, 1 figures, 2 tables, 10th International Conference, AMT 2014, Warsaw, Poland, August 11-14, 2014. Proceeding