Supervised Learning via Ensemble Tensor Completion

Learning nonlinear functions from input-output data pairs is one of the most fundamental problems in machine learning. Recent work has formulated the problem of learning a general nonlinear multivariate function of discrete inputs, as a tensor completion problem with smooth latent factors. We build upon this idea and utilize two ensemble learning techniques to enhance its prediction accuracy. We showcase the effectiveness of the proposed ensemble models on several regression tasks and report significant improvements compared to the single model.