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 [1].
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. |