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..
Generalization for multiclass classification with overparameterized linear models
Authors: Vignesh Subramanian, Rahul Arya, Anant Sahai
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Not really relevant since there are no empirical experiments, only plots of the regions defined by the theorems which are themselves given by simple linear inequalities. |
| Researcher Affiliation | Academia | Vignesh Subramanian Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley, CA-994720, USA EMAIL Rahul Arya Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley, CA-994720, USA EMAIL Anant Sahai Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley, CA-994720, USA EMAIL |
| Pseudocode | No | The paper contains mathematical formulations, equations, and a problem setup description but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | Not really relevant since there are no empirical experiments, only plots of the regions defined by the theorems which are themselves given by simple linear inequalities. |
| Open Datasets | No | The paper describes a theoretical setup with synthetic data generation (i.i.d Gaussian vectors, Assumption 4.1, Bi-level ensemble model 4.2) rather than using a publicly available or open dataset. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not describe experiments involving specific training, validation, or test dataset splits. It models the problem asymptotically. |
| Hardware Specification | No | Not applicable since everything could be done by hand. |
| Software Dependencies | No | Not applicable since everything could be done by hand. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experiment setup with hyperparameters, training configurations, or system-level settings. It defines parameters for its theoretical model (p, q, r, t) but these are not experimental setup details. |