Author

Kyle Ritscher

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#machine learning Preprint Sep 2026

Heteroskedastic Canonical Polyadic Tensor Decomposition

When minimizing the squared-error loss, the popular CP decomposition can be interpreted as parameter inference in a Gaussian model with a low-rank mean tensor and constant variance across the tensor entries. We introduce heteroskedastic-CP (HCP), which models entrywise variability with a non-constant, low-rank precisio...

Kyle Ritscher, Carlos Llosa-Vite · 0 citations