Generalization Analysis for Multi-Label Learning
Authors: Yifan Zhang, Min-Ling Zhang
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Despite great advances in algorithms for multilabel learning, research on the theoretical analysis of generalization is still in the early stage. Some recent theoretical results has investigated the generalization performance of multi-label learning under several evaluation metrics, however, how to reduce the dependency on the number of labels, explicitly introduce label correlations, and quantitatively analyze the impact of various inductive biases in the generalization analysis of multi-label learning is still a crucial and open problem. In an attempt to make up for the gap in the generalization theory of multi-label learning, we develop several novel vector-contraction inequalities, which exploit the Lipschitz continuity of loss functions, and derive generalization bounds with a weaker dependency on the number of labels than the state of the art in the case of decoupling the relationship among different components, which serves as theoretical guarantees for the generalization of multi-label learning. |
| 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 <zhangml@seu.edu.cn>. |
| Pseudocode | No | The paper is theoretical and does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code. |
| Open Datasets | No | This is a theoretical paper and does not use or refer to publicly available datasets for training experiments. The 'dataset D' mentioned in Section 2 is a theoretical construct for defining problem settings. |
| Dataset Splits | No | This is a theoretical paper and does not perform empirical validation on dataset splits. |
| Hardware Specification | No | This is a theoretical paper and does not describe any hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not list any software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe any experimental setup details such as hyperparameters or training configurations. |