3 accepted papers
Neural node embedding has been recently developed as a powerful representation for supervised tasks with graph data. We leverage this recent advance and propose a novel approach for unsupervised community discovery in graphs. Through extensive experimental studies on simulated and real-world data, w…
We propose a topic modeling approach to the prediction of preferences in pairwise comparisons. We develop a new generative model for pairwise comparisons that accounts for multiple shared latent rankings that are prevalent in a population of users. This new model also captures inconsistent user beha…
We propose a novel model for rank aggregation from pairwise comparisons which accounts for a heterogeneous population of inconsistent users whose preferences are different mixtures of multiple shared ranking schemes. By connecting this problem to recent advances in the non-negative matrix factorizat…