Low-degree polynomials lower bounds for clustering in small dimension
arXiv:2610.37272v1 Announce Type: new Abstract: We study the computational limits of clustering $n$ observations drawn from a mixture of $K$ isotropic distributions in $\mathbb R^d$, in the regime where the number $K$ of groups is much larger than the dimension $d$. In this regime, tensor-based pro…
Read the full story at arXiv stat.ML ↗
Timeline · 1 report
- 2026-10-28 04:00 · arXiv stat.ML
Low-degree polynomials lower bounds for clustering in small dimension