On the Error Resistance of Hinge-Loss Minimization
Authors: Kunal Talwar
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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 ktalwar@apple.com 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. |