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stat.ML updates on arXiv.org

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A Smooth Computational Transition in Tensor PCA
Zhangsong Li · 2025-09-12 · via stat.ML updates on arXiv.org

We propose an efficient algorithm for tensor PCA based on counting a specific family of weighted hypergraphs. For the order-$p$ tensor PCA problem where $p \geq 3$ is a fixed integer, we show that when the signal-to-noise ratio is $λn^{-\frac{p}{4}}$ where $λ=Ω(1)$, our algorithm succeeds and runs in time $n^{C+o(1)}$ where $C=C(λ)$ is a constant depending on $λ$. This algorithm improves a poly-logarithmic factor compared to previous algorithms based on the Sum-of-Squares hierarchy \cite{HSS15} or based on the Kikuchi hierarchy in statistical physics \cite{WEM19}. Furthermore, our result shows a smooth tradeoff between the signal-to-noise ratio and the computational cost in this problem, thereby confirming a conjecture posed in \cite{KWB22}.