Nagarajan Natarajan

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In this paper, we theoretically study the problem of binary classification in the presence of random classification noise — the learner, instead of seeing the true labels , sees labels that have independently been flipped with some small probability. Moreover, random label noise is class-conditional — the flip probability depends on the class. We provide(More)
Performance metrics for binary classification are designed to capture tradeoffs between four fundamental population quantities: true positives, false positives, true negatives and false negatives. Despite significant interest from theoretical and applied communities, little is known about either optimal classifiers or consistent algorithms for optimizing(More)
Social network analysis has attracted increasing attention in recent years. In many social networks, besides friendship links amongst users, the phenomenon of users associating themselves with groups or communities is common. Thus, two networks exist simultaneously: the friendship network among users, and the affiliation network between users and groups. In(More)
The study of social networks is a burgeoning research area. However, most existing work is on networks that simply encode whether relationships exist or not. In contrast, relationships in signed networks can be positive (" like " , " trust ") or negative (" dislike " , " distrust "). The theory of social balance shows that signed networks tend to conform to(More)
Multilabel classification is rapidly developing as an important aspect of modern predictive modeling, motivating study of its theoretical aspects. To this end, we propose a framework for constructing and analyzing multilabel classification met-rics which reveals novel results on a parametric form for population optimal clas-sifiers, and additional insight(More)
We consider the problem of link prediction in signed networks. Such networks arise on the web in a variety of ways when users can implicitly or explicitly tag their relationship with other users as positive or negative. The signed links thus created reflect social attitudes of the users towards each other in terms of friendship or trust. Our first(More)
Social network analysis has attracted increasing attention in recent years. In many social networks, besides friendship links among users, the phenomenon of users associating themselves with groups or communities is common. Thus, two networks exist simultaneously: the friendship network among users, and the affiliation network between users and groups. In(More)
The application a smart phone user will launch next intuitively depends on the sequence of apps used <i>recently</i>. More generally, when users interact with systems such as shopping websites or online radio, they click on items that are of interest in the <i>current</i> context. We call the sequence of clicks made in the current session <i>interactional(More)
Network structure and content in microblogging sites like <i>Twitter</i> influence each other ---user <i>A</i> on Twitter <i>follows</i> user <i>B</i> for the <i>tweets</i> that <i>B posts</i> on the network, and <i>A</i> may then <i>re-tweet</i> the content shared by <i>B</i> to his/her own followers. In this paper, we propose a probabilistic model to(More)