Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Generalization Analysis for Label-Specific Representation Learning
Authors: Yi-Fan Zhang, Min-Ling Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 30,869 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,012 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | However, the generalization analysis of LSRL is still in its infancy. The existing theory bounds for multi-label learning, which preserve the coupling among different components, are invalid for LSRL. In an attempt to overcome this challenge and make up for the gap in the generalization theory of LSRL, we develop a novel vector-contraction inequality and derive the generalization bound for general function class of LSRL with a weaker dependency on the number of labels than the state of the art. |
| Researcher Affiliation | Academia | 1 School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China 2 School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 3 Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | This is a purely theoretical work. This paper does not include experiments requiring code. |
| Open Datasets | No | This is a purely theoretical work. This paper does not include experiments. |
| Dataset Splits | No | This is a purely theoretical work. This paper does not include experiments. |
| Hardware Specification | No | This is a purely theoretical work. This paper does not include experiments. |
| Software Dependencies | No | This is a purely theoretical work. This paper does not include experiments. |
| Experiment Setup | No | This is a purely theoretical work. This paper does not include experiments. |