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..
Maximum Margin Multiclass Nearest Neighbors
Authors: Aryeh Kontorovich, Roi Weiss
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop a general framework for marginbased multicategory classification in metric spaces. The basic work-horse is a marginregularized version of the nearest-neighbor classifier. We prove generalization bounds that match the state of the art in sample size n and significantly improve the dependence on the number of classes k. |
| Researcher Affiliation | Academia | Aryeh Kontorovich EMAIL Department of Computer Science, Ben-Gurion University, Beer Sheva 84105, ISRAEL Roi Weiss EMAIL Department of Computer Science, Ben-Gurion University, Beer Sheva 84105, ISRAEL |
| Pseudocode | No | The paper describes algorithmic steps and procedures in text (e.g., in Section 4 'Algorithm') but does not include any formally labeled pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not provide any statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper that focuses on mathematical proofs and algorithmic analysis. It does not use or refer to any specific publicly available datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper and does not involve experimental validation on datasets, thus no training, validation, or test splits are described. |
| Hardware Specification | No | This is a theoretical paper and does not report on conducted experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper and does not involve software implementation details, therefore no specific software dependencies or versions are mentioned. |
| Experiment Setup | No | This is a theoretical paper and does not report on conducted experiments, therefore no experimental setup details, hyperparameters, or training configurations are provided. |