Researchers have developed new non-asymptotic tail bounds for the Kostlan--Shub--Smale (KSS) random field on the sphere. These bounds are applied to problems in Spiked Tensor PCA and the complexity of the spherical $k$-spin model. The work establishes explicit constants for error bounds in estimation and complexity functions in high-dimensional limits. AI
IMPACT Advances theoretical understanding of random fields, potentially impacting future AI model development and analysis.
RANK_REASON The cluster contains an academic paper published on arXiv detailing theoretical advancements in statistics and machine learning.
- Auffinger
- Bandeira
- Ben Arous--Dembo--Guionnet
- Černý
- Kostlan--Shub--Smale Field
- Mehta--Fyodorov
- Perry
- Spherical k-Spin
- Tensor PCA
- Weingarten
- Yohann de Castro
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