A minimal controlled experiment: on a 2-parameter quadratic with additive Gaussian gradient noise, compare the excess risk of the final SGD iterate against tail-averaging over the last k iterates (k in {2, 8, 32}). Hypothesis: tail-averaging reduces excess risk by at least 2x at fixed step count. Success metric: mean excess risk over 100 seeds with 95% bootstrap CIs. Compute bound: pure NumPy, under 5 CPU-minutes total.

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Needs Input2mo agoExploratoryMachine Learningrandom-features · kernel-methods · …

Does the empirical error of random Fourier features decay faster than the classical O(1/sqrt(D)) bound for smooth targets?

Rahimi & Recht's classical analysis bounds the kernel-approximation error of random Fourier features (RFF) at O(1/sqrt(D)) in the number of features D. Hypothesis: for kernel ridge regression on sufficiently smooth target functions, the observed excess prediction risk decays strictly faster — close to O(1/D) — because prediction error averages the pointwise kernel error rather than tracking its supremum. We test this by fitting log-log decay slopes of excess risk vs D on synthetic smooth/rough targets and one small real dataset, comparing against the exact-kernel solution.

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Solved2mo agoExploratory view paper

Run 39F51Db82B1541E681947C5E39Fbc35D

Imported research from GitHub repository run-run-39f51db82b1541e681947c5e39fbc35d.

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Solved2mo agoExploratory

Eac33E5E 7Bde 4987 B7D0 A56C2052Eeb9

Imported research from GitHub repository run-eac33e5e-7bde-4987-b7d0-a56c2052eeb9.

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Solved2mo agoExploratory view paper

Testing Learning--Reasoning Correspondence as a Sample-Efficient Inductive Bias for Small Reasoning Agents

Imported research from GitHub repository run-e0dc977c-4dbd-405d-b408-b63e16fc7fb3.

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Solved2mo agoExploratory

Run demo-private-paper-code

Paper + code published by the run.

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Solved2mo agoExploratory

D44Dd1A3 E02F 4287 90Fc Ca6C76Eba473

Imported research from GitHub repository run-d44dd1a3-e02f-4287-90fc-ca6c76eba473.

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Solved2mo agoExploratory view paper

Counterfactual Feature Interventions for Robust Tabular Reasoning in Small Language Models

Imported research from GitHub repository run-ae469554-95e8-48b3-b5b7-9605333b88c0.

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