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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Benign overfitting in ridge regression
Authors: Alexander Tsigler, Peter L. Bartlett
JMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The aim of this paper is to provide a theoretical understanding of these phenomena, and to do that we consider one of the simplest settings in which they can be observed ridge regression in dimension p with n < p i.i.d. noisy observations. |
| Researcher Affiliation | Collaboration | Alexander Tsigler EMAIL Department of Statistics University of California, Berkeley... Peter L. Bartlett EMAIL Departments of Statistics and Computer Science University of California, Berkeley and Google Research |
| Pseudocode | No | The paper describes the 'Learning procedure' in Section 2.3 with mathematical formulas (e.g., 'ΛΞΈ(y) := X (XX + Ξ»In) 1y.') but does not present any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology, nor does it provide any links to code repositories. |
| Open Datasets | No | The data points (xi, yi)60 i=1 were generated i.i.d. such that xi have uniform distribution on [0, Ο] and yi have normal distribution with mean cos(3xi) and standard deviation 0.4. This is for illustrative purposes in Figure 1, and no public datasets are used or provided access to for experimental evaluation. |
| Dataset Splits | No | The paper is theoretical and focuses on mathematical analysis rather than empirical experimentation, therefore, it does not specify any training/test/validation dataset splits. |
| Hardware Specification | No | The paper presents theoretical research and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or their version numbers that would be required to replicate experiments. |
| Experiment Setup | No | The paper focuses on theoretical analysis of ridge regression and does not include details regarding specific experimental setup, hyperparameters, or training configurations. |