Tight and Fast Bounds for Multi-Label Learning
Authors: Yi-Fan Zhang, Min-Ling Zhang
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our state-of-the-art theoretical results provide general theoretical guarantees for the generalization of multi-label learning. In this paper, we derive tight bounds with no dependency on c and a faster convergence rate w.r.t. n for multi-label learning, which connects the smoothness of base losses with the tighter and faster bounds. Major contributions of the paper include: We develop novel vector-contraction inequalities for smooth base losses... We develop novel local vector-contraction inequalities for smooth base losses... We derive tight bounds with no dependency on c for Macro-Averaged AUC... |
| Researcher Affiliation | Academia | 1School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China 2Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China 3School of Computer Science and Engineering, Southeast University, Nanjing 210096, China. Correspondence to: Min-Ling Zhang <EMAIL>. |
| Pseudocode | No | The paper focuses on theoretical derivations, proofs, and inequalities related to generalization bounds for multi-label learning. It describes methods and contributions in paragraph form but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, links to code repositories, or mention of code in supplementary materials for the described methodology. The 'Conclusion' section mentions 'In future work, we will consider experimental verification' but not code release. |
| Open Datasets | No | The paper discusses multi-label learning in a theoretical context and refers to a 'dataset D = {(x1, y1) , . . . , (xn, yn)}' as a general concept for theoretical analysis in its preliminaries section. However, no specific dataset names, links, DOIs, or citations to publicly available datasets are provided, as no empirical studies are conducted. |
| Dataset Splits | No | The paper is theoretical and does not describe any experimental evaluation involving specific datasets or their splits (e.g., training, validation, test sets). |
| Hardware Specification | No | The paper focuses purely on theoretical analysis and does not describe any experiments. Therefore, there is no mention of hardware specifications used for running experiments. |
| Software Dependencies | No | The paper is entirely theoretical and does not describe any experimental setup or implementation details that would require specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs and derivations of generalization bounds. It does not include any experimental section, hyperparameters, training configurations, or other system-level settings for reproducing experiments. |