Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality
Authors: Marko Medvedev, Gal Vardi, Nati Srebro
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A Empirical Justification. We plot the dependence of the test error of the Gaussian kernel ridgeless predictor... We ran the experiments on one A6000 GPU. |
| Researcher Affiliation | Academia | Marko Medvedev The University of Chicago medvedev@uchicago.edu Gal Vardi Weizmann Institute of Science gal.vardi@weizmann.ac.il Nathan Srebro TTI-Chicago nati@ttic.edu |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | The code to reproduce these experiments can be found at https://github.com/marko-medvedev/overfitting-behavior-of-gaussian-kernel-ridgeless-regression. |
| Open Datasets | No | The paper uses synthetic data generated as 'y = f (x) + ξ where ξ N(0, σ2), f = 10, σ2 is the noise level, and x Unif(Sd 1)', which is not a publicly available or open dataset. |
| Dataset Splits | No | The paper describes experiments but does not provide specific training/test/validation dataset splits. It mentions running '100 different runs of the experiment' but no explicit data partitioning. |
| Hardware Specification | Yes | We ran the experiments on one A6000 GPU. |
| Software Dependencies | No | The paper provides a link to the code for reproduction but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | Specifically, we consider y = f (x) + ξ where ξ N(0, σ2), f = 10, σ2 is the noise level, and x Unif(Sd 1). We vary the values of d and σ2 and the bandwidth scaling τm as follows: for τm = o(m 1 d 1 ) we take σ2 = 1 and d = 6, for τm = ω(m 1 d 1 ) we take σ2 = 10 and d = 4, and for τm = Θ(m 1 d 1 ) we take σ2 = 10000 and d = 6. |