Multi-way Interacting Regression via Factorization Machines

NeurIPS 2017 Mikhail YurochkinXuanLong NguyenNikolaos Vasiloglou

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... (read more)

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