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..
Reducing Adversarially Robust Learning to Non-Robust PAC Learning
Authors: Omar Montasser, Steve Hanneke, Nati Srebro
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the problem of reducing adversarially robust learning to standard PAC learning, i.e. the complexity of learning adversarially robust predictors using access to only a black-box non-robust learner. We give a reduction that can robustly learn any hypothesis class C using any non-robust learner A for C. The number of calls to A depends logarithmically on the number of allowed adversarial perturbations per example, and we give a lower bound showing this is unavoidable. |
| Researcher Affiliation | Academia | Omar Montasser EMAIL Steve Hanneke EMAIL Nathan Srebro EMAIL Toyota Technological Institute at Chicago |
| Pseudocode | Yes | Algorithm 1: Robustify The Non-Robust |
| Open Source Code | No | The paper does not provide any links to open-source code or state that code will be released. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training on specific datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on specific datasets, hence no training/validation/test splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers for experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |