Statistical Limits for Finite-Rank Tensor Estimation

Riccardo Rossetti; Galen Reeves

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arXiv · 2506.06749

Statistical Limits for Finite-Rank Tensor Estimation

Riccardo Rossetti·Galen Reeves

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Abstract

This paper provides a unified framework for analyzing tensor estimation problems that allow for nonlinear observations, heteroskedastic noise, and covariate information. We study a general class of high-dimensional models where each observation depends on the interactions among a finite number of unknown parameters. Our main results provide asymptotically exact formulas for the mutual information (equivalently, the free energy) as well as the minimum mean-squared error in the Bayes-optimal setting. We then apply this framework to derive sharp characterizations of statistical thresholds for two novel scenarios: (1) tensor estimation in heteroskedastic noise that is independent but not identically distributed, and (2) higher-order assignment problems, where the goal is to recover an unknown permutation from tensor-valued observations.

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Riccardo Rossetti, Galen Reeves. 2025-06-07. Statistical Limits for Finite-Rank Tensor Estimation. https://arxiv.org/abs/2506.06749

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