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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Exploiting the Surrogate Gap in Online Multiclass Classification
Authors: Dirk van der Hoeven
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our contribution is primarily theoretical. Therefore, this work does not present any foreseeable societal consequence. |
| Researcher Affiliation | Academia | Dirk van der Hoeven Mathematical Institute Leiden University EMAIL |
| Pseudocode | Yes | Algorithm 1 GAPTRON |
| Open Source Code | No | The paper does not provide any links to open-source code for the described methodology. The conclusion states: "In future work we would like to conduct experiments to compare GAPTRON with other algorithms," implying code is not yet publicly available. |
| Open Datasets | No | The paper is theoretical and does not perform experiments on datasets, therefore no public dataset information is provided. |
| Dataset Splits | No | The paper is theoretical and does not perform experiments on datasets, therefore no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments, thus no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not report on experiments, thus no experimental setup details like hyperparameters or training settings are provided. |