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 Existence of The Adversarial Bayes Classifier
Authors: Pranjal Awasthi, Natalie Frank, Mehryar Mohri
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we study a fundamental question regarding Bayes optimality for adversarial robustness. We provide general sufficient conditions under which the existence of a Bayes optimal classifier can be guaranteed for adversarial robustness. Our results can provide a useful tool for a subsequent study of surrogate losses in adversarial robustness and their consistency properties. |
| Researcher Affiliation | Collaboration | Pranjal Awasthi Google Research New York, NY 10011, USA EMAIL Natalie S. Frank Courant Institute New York, NY 10012 EMAIL Mehryar Mohri Google Research & Courant Institute New York, NY 10011, USA EMAIL |
| Pseudocode | No | No, the paper is theoretical and does not present any pseudocode or algorithm blocks. |
| Open Source Code | No | No, the paper is theoretical and does not describe a computational methodology for which open-source code would be provided. |
| Open Datasets | No | No, the paper is theoretical and does not use or reference any publicly available or open datasets for empirical evaluation. |
| Dataset Splits | No | No, the paper is theoretical and does not involve dataset splits for empirical evaluation. |
| Hardware Specification | No | No, the paper is theoretical and does not conduct experiments, so no hardware specifications are provided. |
| Software Dependencies | No | No, the paper is theoretical and does not describe any computational experiments or implementations that would require specific software dependencies. |
| Experiment Setup | No | No, the paper is theoretical and does not describe an experimental setup, hyperparameters, or training settings. |