Heteroskedastic Canonical Polyadic Tensor Decomposition

arXiv:2610.00498 · stat.ME, cs.LG, stat.ML · Submitted 2026-09-30 · Read on arXiv

stat.ME, cs.LG, stat.ML

Submitted: 2026-09-30

Updated: 2026-10-09

Comments: 35 pages, 16 figures

Code: https://github.com/sandialabs/pyttb

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Degeneracy — 9× in this paper · explained in 3 episodes
• Core — 4× in this paper · explained in 2 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Objective Function — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Longitudinal Data — 1× in this paper · explained in 2 episodes
• Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• Statistical Inference for Low-Rank Tensors: Heteroskedasticity, Subgaussianity, and Applications
• Fundamental limits for rank-one matrix estimation with groupwise heteroskedasticity
• Near-Universal Multiplicative Updates for Nonnegative Einsum Factorization

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