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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
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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 · Regression · Latent Variable Models
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Author Information

Mikhail Yurochkin (IBM Research AI)

I am a Research Staff Member at IBM Research and MIT-IBM Watson AI Lab in Cambridge, Massachusetts. My research interests are - Algorithmic Fairness - Out-of-Distribution Generalization - Applications of Optimal Transport in Machine Learning - Model Fusion and Federated Learning Before joining IBM, I completed my PhD in Statistics at the University of Michigan, where I worked with Long Nguyen. I received my Bachelor's degree in applied mathematics and physics from Moscow Institute of Physics and Technology.

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

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