The recent emergence of Graphics Processing Units (GPUs) as general-purpose parallel
computing devices provides us with new opportunities to develop scalable learning methods …

Abstract Electronic Health Records (EHRs) contain rich clinical data collected at the point of
the care, and their increasing adoption offers exciting opportunities for clinical informatics …

Businesses and organizations in jurisdictions around the world are required by law to
provide their customers and users with information about their business practices in the form …

No agent has the resources to monitor all events that are potentially relevant for his
decisions. Therefore, many delegate their information choice to specialized news providers …

The social sciences and digital humanities have recently adopted the machine learning
technique of topic modeling to address research questions in their fields. This is problematic …

Stochastic variational inference finds good posterior approximations of probabilistic models
with very large data sets. It optimizes the variational objective with stochastic optimization …

Probabilistic topic models are powerful tools for discovering hidden structures/semantics in
discrete data, eg, texts, images, links. However, on short and noisy texts, directly applying …

Developing efficient and scalable algorithms for Latent Dirichlet Allocation (LDA) is of wide
interest for many applications. Previous work has developed an $ O (1) $ Metropolis …

Stochastic variational inference makes it possible to approximate posterior distributions
induced by large datasets quickly using stochastic optimization. The algorithm relies on the …

Variational methods provide a computationally scalable alternative to Monte Carlo methods
for large-scale, Bayesian nonparametric learning. In practice, however, conventional batch …