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 Bounds for Regularized Pairwise Learning
Authors: Yunwen Lei, Shao-Bo Lin, Ke Tang
IJCAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 18,334 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,044 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 | In this paper, we establish a unified generalization error bound for regularized pairwise learning without either Bernstein conditions or capacity assumptions. We apply this general result to typical learning tasks including distance metric learning and ranking, for each of which our discussion is able to improve the state-of-the-art results. |
| Researcher Affiliation | Academia | 1 Shenzhen Key Laboratory of Computational Intelligence, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 2 Department of Mathematics, Wenzhou University, Wenzhou |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper; it focuses on theoretical derivations. |
| Open Source Code | No | No statement regarding the release of open-source code or links to repositories for the methodology described in this paper. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, thus no information on dataset availability for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments; therefore, it does not provide details on training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental procedures that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe experimental procedures that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail an experimental setup, including hyperparameters or system-level training settings. |