Implicit Regularization of Random Feature Models
Authors: Arthur Jacot, Berfin Simsek, Francesco Spadaro, Clement Hongler, Franck Gabriel
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically find an extremely good agreement between the test errors of the average λ-RF predictor and λ-KRR predictor. |
| Researcher Affiliation | Academia | 1Chair of Statistical Field Theory, Ecole Polytechnique F ed erale de Lausanne, Lausanne, Switzerland 2Laboratory of Computational Neuroscience, Ecole Polytechnique F ed erale de Lausanne, Lausanne, Switzerland. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing code or links to repositories for the described methodology. |
| Open Datasets | Yes | We train the RF predictors on N = 100 MNIST data points where K is the RBF kernel, i.e. K(x, x ) = exp x x 2/ℓ . |
| Dataset Splits | No | The paper mentions using N=100 MNIST data points for training and 100 random test points, but it does not specify explicit training/validation/test splits, percentages, or a cross-validation setup. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for the experiments (e.g., GPU models, CPU types, or cloud instances). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | No | The paper mentions using N=100 MNIST data points and the RBF kernel, as well as ranges for lambda in figures, but it lacks comprehensive details on the experimental setup such as specific hyperparameter values (e.g., learning rate, batch size, optimizer), model initialization, or training schedules. |