pith:RPHRCEJL
Average Gradient Outer Product in kernel regression provably recovers the central subspace for multi-index models
The average gradient outer product from kernel ridge regression recovers the central subspace of multi-index models in sample regimes too small for accurate prediction.
arxiv:2605.15082 v1 · 2026-05-14 · stat.ML · cs.LG · math.ST · stat.TH
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Claims
the top r-dimensional eigenspace of AGOP provably recovers the central subspace, even in regimes when the prediction error remains large. Specifically, if the target function f* has degree p*, ... subspace recovery occurs in the much lower sample regime n ≍ d^{p+δ} for any δ ∈ (0,1).
a low degree p component of f* already carries all relevant directions for prediction, together with unspecified 'reasonable assumptions' on the kernel and the multi-index structure (abstract only).
For multi-index polynomials, the top r eigenspace of the AGOP matrix from KRR recovers the central subspace at sample complexity n ~ d^{p+δ} where p is the degree of the informative component.
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| First computed | 2026-05-17T21:40:25.977506Z |
|---|---|
| Last reissued | 2026-05-17T21:57:19.284907Z |
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | unsigned_v0 |
| Schema | pith-number/v1.0 |
Canonical hash
8bcf11112b0e36605bed3e8df727571c35cd04e23f6389a3e1f08a728a002ff2
Aliases
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Canonical record JSON
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