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Spectral Methods for Indian Buffet Process Inference
Hsiao-Yu Tung · Alexander Smola

Wed Dec 10 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D

The Indian Buffet Process is a versatile statistical tool for modeling distributions over binary matrices. We provide an efficient spectral algorithm as an alternative to costly Variational Bayes and sampling-based algorithms. We derive a novel tensorial characterization of the moments of the Indian Buffet Process proper and for two of its applications. We give a computationally efficient iterative inference algorithm, concentration of measure bounds, and reconstruction guarantees. Our algorithm provides superior accuracy and cheaper computation than comparable Variational Bayesian approach on a number of reference problems.

Author Information

Hsiao-Yu Tung (Carnegie Mellon University)
Alexander Smola (Amazon)

**AWS Machine Learning**

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