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Dirichlet Simplex Nest and Geometric Inference
Mikhail Yurochkin · Aritra Guha · Yuekai Sun · XuanLong Nguyen

Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #230

We propose Dirichlet Simplex Nest, a class of probabilistic models suitable for a variety of data types, and develop fast and provably accurate inference algorithms by accounting for the model's convex geometry and low dimensional simplicial structure. By exploiting the connection to Voronoi tessellation and properties of Dirichlet distribution, the proposed inference algorithm is shown to achieve consistency and strong error bound guarantees on a range of model settings and data distributions. The effectiveness of our model and the learning algorithm is demonstrated by simulations and by analyses of text and financial data.

Author Information

Mikhail Yurochkin (IBM Research, MIT-IBM Watson AI Lab)
Aritra Guha (U Michigan)
Yuekai Sun (University of Michigan)
XuanLong Nguyen (University of Michigan)

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