Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
How rotational invariance of common kernels prevents generalization in high dimensions
Authors: Konstantin Donhauser, Mingqi Wu, Fanny Yang
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we describe our synthetic and real-world experiments to further illustrate our theoretical results and underline the importance of feature selection in high dimensional kernel learning. |
| Researcher Affiliation | Academia | 1Department of Computer Science, ETH Z urich. Correspondence to: Konstantin Donhauser <EMAIL>. |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the release of open-source code for the described methodology. |
| Open Datasets | Yes | In this section we show results on the regression dataset residential housing (RH) with n = 372 and d = 107 to predict sales prices from the UCI website (Dua and Graff, 2017). |
| Dataset Splits | Yes | We use a random 80/20 train/test split, and the data is scaled to zero mean and unit variance for each dimension separately. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We primarily use the Laplace kernel with τ = tr(Σd) unless otherwise specified and study two sparse monomials as ground truth functions, f 1 (x) = 2x2 (1) and f 2 (x) = 2x3 (1)... In order to estimate the bias EY ˆf0 f 2 L2(PX) of the minimum norm interpolant we fit noiseless observations and approximate the expected squared error using 10000 i.i.d. test samples. |