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..
On the Error Resistance of Hinge-Loss Minimization
Authors: Kunal Talwar
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | we identify a set of conditions on the data under which such surrogate loss minimization algorithms provably learn the correct classifier. This allows us to establish, in a unified framework, the robustness of these algorithms under various models on data as well as error.Our proof relates the optimality conditions to the 0-1 loss of the resulting classifier. |
| Researcher Affiliation | Industry | Kunal Talwar Apple Cupertino, CA 95014 EMAIL Work performed while at Google Brain. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper focuses on theoretical analysis of data distributions and samples, and does not mention or provide access information for any specific publicly available training dataset. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments, therefore no specific training, validation, or test dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe running experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe running experiments, therefore no software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe running experiments, therefore no specific experimental setup details such as hyperparameters are provided. |