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Scalable inference of topic evolution via models for latent geometric structures
Mikhail Yurochkin · Zhiwei Fan · Aritra Guha · Paraschos Koutris · XuanLong Nguyen

Wed Dec 11 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #190

We develop new models and algorithms for learning the temporal dynamics of the topic polytopes and related geometric objects that arise in topic model based inference. Our model is nonparametric Bayesian and the corresponding inference algorithm is able to discover new topics as the time progresses. By exploiting the connection between the modeling of topic polytope evolution, Beta-Bernoulli process and the Hungarian matching algorithm, our method is shown to be several orders of magnitude faster than existing topic modeling approaches, as demonstrated by experiments working with several million documents in under two dozens of minutes.

Author Information

Mikhail Yurochkin (IBM Research, MIT-IBM Watson AI Lab)
Zhiwei Fan (University of Wisconsin-Madison)
Aritra Guha (University of Michigan)
Paraschos Koutris (University of Wisconsin-Madison)
XuanLong Nguyen (University of Michigan)

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