Large Scale Tensor Regression using Kernels and Variational Inference

2020/02/11 by Robert Hu, Hu, Robert, Geoff K. Nicholls +3
Computer Science · Mathematics · #Algorithms and Data Compression #Computation (stat.CO) #Computational Physics and Python Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Tensor decomposition and applications

paper · pdf · doi:10.48550/arxiv.2002.04704

openalex publication_date 2020/02/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We outline an inherent weakness of tensor factorization models when latent factors are expressed as a function of side information and propose a novel method to mitigate this weakness. We coin our method Kernel Fried Tensor(KFT) and present it as a large scale forecasting tool for high dimensional data. Our results show superior performance against LightGBM and Field Aware Factorization Machines(FFM), two algorithms with proven track records widely used in industrial forecasting. We also develop a variational inference framework for KFT and associate our forecasts with calibrated uncertainty estimates on three large scale datasets. Furthermore, KFT is empirically shown to be robust against uninformative side information in terms of constants and Gaussian noise.

Citations

• Tensor Decompositions and Applications
• Collaborative Filtering with Side Information: a Gaussian Process Perspective

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