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 Margin Maximization in Linear and ReLU Networks
Authors: Gal Vardi, Ohad Shamir, Nati Srebro
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper is purely theoretical in nature, and we do not see any potential negative societal impacts that should be discussed. (b) Did you include complete proofs of all theoretical results? [Yes] |
| Researcher Affiliation | Academia | Gal Vardi TTI-Chicago and Hebrew University EMAIL Ohad Shamir Weizmann Institute of Science EMAIL Nathan Srebro TTI-Chicago EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and explicitly states 'N/A' for questions related to providing code for experimental results. There is no mention of releasing source code for the methodology described. |
| Open Datasets | No | The paper is purely theoretical and does not describe any empirical studies involving training on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments, thus no dataset split information for validation is provided. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is purely theoretical and does not mention specific ancillary software or their version numbers. |
| Experiment Setup | No | The paper is purely theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |