Statistical models of text have become increasingly popular in statistics and computer
science as a method of exploring large document collections. Social scientists often want to …

We focus on modeling human activities comprising multiple actions in a completely
unsupervised setting. Our model learns the high-level action co-occurrence and temporal …

We present a scalable Bayesian multi-label learning model based on learning low-
dimensional label embeddings. Our model assumes that each label vector is generated as a …

Reviews are collaboratively generated by users on items and generally contain rich
information than ratings in a recommender system scenario. Ratings are modeled …

Relational model learning is useful for numerous practical applications. Many algorithms
have been proposed in recent years to tackle this important yet challenging problem …

There is a large variation in the activities that humans perform in their everyday lives. We
consider modeling these composite human activities which comprises multiple basic level …

Considering the hierarchical data groupings in text corpus, eg, words, sentences, and
documents, we conduct the structural learning and infer the latent themes and topics for …

We introduce the nonparametric metadata dependent relational (NMDR) model, a Bayesian
nonparametric stochastic block model for network data. The NMDR allows the entities …

Since the 18th century, the novel has been one of the defining forms of English writing, a
mainstay of popular entertainment and academic criticism. Despite its importance, however …

This work focuses on combining nonparametric topic models with Auto-Encoding Variational
Bayes (AEVB). Specifically, we first propose iTM-VAE, where the topics are treated as …