We investigate the use of unlabeled data to help labeled data in classification. We propose a simple iterative algorithm, label propagation, to propagate labels through the dataset along high densityâ€¦ (More)

Active and semi-supervised learning are important techniques when labeled data are scarce. We combine the two under a Gaussian random field model. Labeled and unlabeled data are represented asâ€¦ (More)

We introduce Time-Sensitive Dirichlet Process Mixture models for clustering. The models allow infinite mixture components just like standard Dirichlet process mixture models. However they also haveâ€¦ (More)

Latent Dirichlet Allocation is an unsupervised graphical model which can discover latent topics in unlabeled data. We propose a mechanism for adding partial supervision, called topic-in-setâ€¦ (More)

We show that the Gaussian random fields and harmonic energy minimizing function framework for semi-supervised learning can be viewed in terms of Gaussian processes, with covariance matrices derivedâ€¦ (More)

We demonstrate that subjective creativity in sentence-writing can in part be predicted using computable quantities studied in Computer Science and Cognitive Psychology. We introduce a task in which aâ€¦ (More)

Kernel conditional random fields are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models is given which shows howâ€¦ (More)

Bullying is a serious national health issue among adolescents. Social media offers a new opportunity to study bullying in both physical and cyber worlds. Sentiment analysis has the potential toâ€¦ (More)

Latent Dirichlet Allocation models a document by a mixture of topics, where each topic itself is typically modeled by a unigram word distribution. Documents however often have known structures, andâ€¦ (More)

This paper investigates the problem of active learning for binary label prediction on a graph. We introduce a simple and label-efficient algorithm called Sfor this task. At each step, Sselects theâ€¦ (More)