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].
Near-Tight Margin-Based Generalization Bounds for Support Vector Machines
Authors: Allan Grønlund, Lior Kamma, Kasper Green Larsen
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we revisit and improve the classic generalization bounds in terms of margins. Furthermore, we complement our new generalization bound by a nearly matching lower bound, thus almost settling the generalization performance of SVMs in terms of margins. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Aarhus University, Denmark. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical proofs; it does not use or provide information about publicly available datasets for training experiments. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation or dataset splits for such purposes. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |