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 Hardness of Robust Classification
Authors: Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we study the feasibility of robust learning from the perspective of computational learning theory, considering both sample and computational complexity. |
| Researcher Affiliation | Academia | Pascale Gourdeau University of Oxford EMAIL Varun Kanade University of Oxford EMAIL Marta Kwiatkowska University of Oxford EMAIL James Worrell University of Oxford EMAIL |
| Pseudocode | No | The paper contains mathematical definitions, lemmas, theorems, and proofs, but no pseudocode or explicitly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper discusses theoretical concepts of sampling and distributions (e.g., 'polynomial-size sample from the unknown distribution', 'uniform distribution on {0, 1}n') within its proofs, but does not use or provide concrete access information for publicly available datasets for empirical training. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments that would require training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experiments or their setup, thus no hyperparameter values or training configurations are provided. |