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..
The Adversarial Consistency of Surrogate Risks for Binary Classification
Authors: Natalie Frank, Jonathan Niles-Weed
NeurIPS 2023 | Venue PDF | 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 EMAIL Jonathan Niles-Weed Courant Institute New York University New York, NY 10012 EMAIL |
| 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. |