On the Hardness of Robust Classification
Authors: Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 pascale.gourdeau@cs.ox.ac.uk Varun Kanade University of Oxford varunk@cs.ox.ac.uk Marta Kwiatkowska University of Oxford marta.kwiatkowska@cs.ox.ac.uk James Worrell University of Oxford james.worrell@cs.ox.ac.uk |
| 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. |