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Latent Dirichlet analysis, or topic modeling, is a flexible latent variable framework for modeling high-dimensional sparse count data. Various learning algorithms have been developed in recent years,… (More)

- David Newman, Arthur U. Asuncion, Padhraic Smyth, Max Welling
- Journal of Machine Learning Research
- 2009

We describe distributed algorithms for two widely-used topic models, namely the Latent Dirichlet Allocation (LDA) model, and the Hierarchical Dirichet Process (HDP) model. In our distributed… (More)

In this paper we introduce a novel collapsed Gibbs sampling method for the widely used latent Dirichlet allocation (LDA) model. Our new method results in significant speedups on real world text… (More)

- David Newman, Arthur U. Asuncion, Padhraic Smyth, Max Welling
- NIPS
- 2007

We investigate the problem of learning a widely-used latent-variable model – the Latent Dirichlet Allocation (LDA) or “topic” model – using distributed computation, where each of processors only sees… (More)

- Arthur U. Asuncion, Padhraic Smyth, Max Welling
- NIPS
- 2008

Distributed learning is a problem of fundamental interest in machine learning and cognitive science. In this paper, we present asynchronous distributed learning algorithms for two well-known… (More)

- Hazeline U. Asuncion, Arthur U. Asuncion, Richard N. Taylor
- 2010 ACM/IEEE 32nd International Conference on…
- 2010

Software traceability is a fundamentally important task in software engineering. The need for automated traceability increases as projects become more complex and as the number of artifacts… (More)

- Ian Porteous, Arthur U. Asuncion, Max Welling
- AAAI
- 2010

Matrix factorization is a fundamental technique in machine learning that is applicable to collaborative filtering, information retrieval and many other areas. In collaborative filtering and many… (More)

- Brynjar Gretarsson, John O'Donovan, +4 authors Padhraic Smyth
- ACM TIST
- 2012

We present <i>TopicNets</i>, a Web-based system for visual and interactive analysis of large sets of documents using statistical topic models. A range of visualization types and control mechanisms to… (More)

Real-world relational data sets, such as social networks, often involve measurements over time. We propose a Bayesian nonparametric latent feature model for such data, where the latent features for… (More)

The analysis of the formation and evolution of networks over time is of fundamental importance to social science, biology, and many other fields. While longitudinal network data sets are increasingly… (More)