The Adversarial Consistency of Surrogate Risks for Binary Classification
Authors: Natalie Frank, Jonathan Niles-Weed
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We give a simple and complete characterization of the set of surrogate loss functions that are consistent... We also prove a quantitative version of adversarial consistency for the -margin loss. Our results reveal that the class of adversarially consistent surrogates is substantially smaller than in the standard setting, where many common surrogates are known to be consistent. |
| Researcher Affiliation | Academia | Natalie S. Frank Courant Institute New York University New York, NY 10012 nf1066@nyu.edu Jonathan Niles-Weed Courant Institute New York University New York, NY 10012 jnw@cims.nyu.edu |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. It focuses on theoretical proofs and mathematical derivations. |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using datasets. No dataset is mentioned as publicly available. |
| Dataset Splits | No | The paper is theoretical and does not describe any dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup involving specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup involving specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, hyperparameters, or training configurations. |