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..
Tight Generalization Bounds for Large-Margin Halfspaces
Authors: Kasper Green Larsen, Natascha Schalburg
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove the first generalization bound for large-margin halfspaces that is asymptotically tight in the tradeoff between the margin, the fraction of training points with the given margin, the failure probability and the number of training points. The main theorem is proved in section 3 and all deferred proofs and reduction arguments have explicit directions to their position in appendices. A proof overview is also provided. |
| Researcher Affiliation | Academia | Kasper Green Larsen Department of Computer Science Aarhus University Aarhus, Denmark EMAIL Natascha Schalburg Department of Computer Science Aarhus University Aarhus, Denmark EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It is a theoretical paper focusing on mathematical proofs and analyses. |
| Open Source Code | No | Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [NA] Justification: The paper does not include experiments. |
| Open Datasets | No | Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [NA] Justification: The paper does not include experiments. |
| Dataset Splits | No | The paper does not include experiments, therefore there is no discussion of dataset splits. |
| Hardware Specification | No | For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments? Answer: [NA] Justification: The paper does not include experiments. |
| Software Dependencies | No | The paper does not include experiments, therefore there is no mention of specific software dependencies with version numbers. |
| Experiment Setup | No | Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [NA] Justification: The paper does not include experiments. |