Large Scale Tensor Regression using Kernels and Variational Inference
This work addresses a specific problem in large-scale forecasting for high-dimensional data, offering a robust method with uncertainty quantification, but it appears incremental as it builds on existing tensor and kernel techniques.
The authors tackled the weakness of tensor factorization models when latent factors depend on side information by proposing Kernel Fried Tensor (KFT), which achieved superior forecasting performance against LightGBM and FFM on large-scale datasets and provided calibrated uncertainty estimates.
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 \textit{Kernel Fried Tensor}(KFT) and present it as a large scale forecasting tool for high dimensional data. Our results show superior performance against \textit{LightGBM} and \textit{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.