Stability-Based Generalization Analysis for Mixtures of Pointwise and Pairwise Learning
Authors: Jiahuan Wang, Jun Chen, Hong Chen, Bin Gu, Weifu Li, Xin Tang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we try to fill this theoretical gap by investigating the generalization properties of PPL. After extending the definitions of algorithmic stability to the PPL setting, we establish the high-probability generalization bounds for uniformly stable PPL algorithms. Moreover, explicit convergence rates of stochastic gradient descent (SGD) and regularized risk minimization (RRM) for PPL are stated by developing the stability analysis technique of pairwise learning. In addition, the refined generalization bounds of PPL are obtained by replacing uniform stability with on-average stability. |
| Researcher Affiliation | Collaboration | 1College of Science, Huazhong Agricultural University, Wuhan 430070, China 2College of Informatics, Huazhong Agricultural University, Wuhan 430070, China 3Engineering Research Center of Intelligent Technology for Agriculture, Ministry of Education, Wuhan 430070, China 4Key Laboratory of Smart Farming for Agricultural Animals, Wuhan 430070, China 5Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE 6Ping An Property & Casualty Insurance Company, Shenzhen, China |
| Pseudocode | No | The paper describes algorithms (like SGD updates in Eq. 6) but does not present them in a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide any statement about releasing open-source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on generalization analysis, not empirical experiments. Therefore, it does not mention specific training datasets or their availability. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments requiring validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require specific software versions or dependencies to replicate. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical analysis; it does not include details about an experimental setup, hyperparameters, or training configurations. |