Generalization for multiclass classification with overparameterized linear models
Authors: Vignesh Subramanian, Rahul Arya, Anant Sahai
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 vignesh.subramanian@eecs.berkeley.edu Rahul Arya Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley, CA-994720, USA rahularya@berkeley.edu Anant Sahai Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley, CA-994720, USA sahai@eecs.berkeley.edu |
| 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. |