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Poster
Multi-way Interacting Regression via Factorization Machines
Mikhail Yurochkin · XuanLong Nguyen · nikolaos Vasiloglou

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #43 #None
in Posters Mon »

We propose a Bayesian regression method that accounts for multi-way interactions of arbitrary orders among the predictor variables. Our model makes use of a factorization mechanism for representing the regression coefficients of interactions among the predictors, while the interaction selection is guided by a prior distribution on random hypergraphs, a construction which generalizes the Finite Feature Model. We present a posterior inference algorithm based on Gibbs sampling, and establish posterior consistency of our regression model. Our method is evaluated with extensive experiments on simulated data and demonstrated to be able to identify meaningful interactions in applications in genetics and retail demand forecasting.

Keywords: Model Selection and Structure Learning · Latent Variable Models · Regression

Author Information

Mikhail Yurochkin (IBM Research AI)

I am working as a Research Staff Member at the IBM Research AI in Cambridge. Before, I have completed PhD in Statistics at the University of Michigan, advised by Prof. Long Nguyen. I received my bachelor degree in applied mathematics and physics from Moscow Institute of Physics and Technology.

Long Nguyen (University of Michigan)
nikolaos Vasiloglou (LogicBlox)

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