2 research outputs found
Large Scale Tensor Regression using Kernels and Variational Inference
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
Large scale tensor regression using kernels and variational inference
We outline an inherent flaw of tensor factorization models when latent
factors are expressed as a function of side information and propose a novel method
to mitigate this. We coin our methodology Kernel Fried Tensor (KFT) and present
it as a large-scale prediction and 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 largescale prediction. We also develop a variational inference framework for KFT
which enables associating the predictions and forecasts with calibrated uncertainty
estimates on several datasets