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 [1].
The Implicit Bias of Benign Overfitting
Authors: Ohad Shamir
JMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we provide several new results on when one can or cannot expect benign overfitting to occur, for both regression and classification tasks. We consider a prototypical and rather generic data model for benign overfitting of linear predictors... We prove that the max-margin predictor... is asymptotically biased towards minimizing a weighted squared hinge loss. This allows us to reduce the question of benign overfitting in classification to the simpler question of whether this loss is a good surrogate for the misclassification error, and use it to show benign overfitting in some new settings. The formal proofs of all our results appear in Appendix A. |
| Researcher Affiliation | Academia | Ohad Shamir EMAIL Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot, Israel |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. Methods are described through mathematical formulations and proofs. |
| Open Source Code | No | The paper does not provide any concrete access to source code or explicitly state that code for the methodology described is available. |
| Open Datasets | No | The paper focuses on theoretical analysis using a 'prototypical and rather generic data model' and theoretical distributions (e.g., 'independent zero-mean Gaussian with covariance matrix 1/d_k I' in Example 1). It does not mention or provide access information for any publicly available datasets used for experimental evaluation. |
| Dataset Splits | No | The paper describes theoretical models and mathematical proofs, not empirical experiments. Therefore, it does not specify any training/test/validation dataset splits. |
| Hardware Specification | No | This paper is theoretical and does not report on any empirical experiments. Therefore, no hardware specifications are mentioned for running experiments. |
| Software Dependencies | No | This paper is theoretical and does not report on any empirical experiments. Therefore, no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | This paper focuses on theoretical analysis and does not describe any empirical experimental setup, hyperparameters, or training configurations. |