Sharper Generalization Bounds for Pairwise Learning
Authors: Yunwen Lei, Antoine Ledent, Marius Kloft
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we provide a reļ¬ned stability analysis by developing generalization bounds which can be n-times faster than the existing results, where n is the sample size. This implies excess risk bounds of the order O(n 1/2) (up to a logarithmic factor) for both regularized risk minimization and stochastic gradient descent. We also introduce a new on-average stability measure to develop optimistic bounds in a low noise setting. We apply our results to ranking and metric learning, and clearly show the advantage of our generalization bounds over the existing analysis. |
| Researcher Affiliation | Academia | Yunwen Lei1,2 Antoine Ledent2 Marius Kloft2 1School of Computer Science, University of Birmingham, Birmingham B15 2TT, United Kingdom 2Department of Computer Science, TU Kaiserslautern, Kaiserslautern 67653, Germany |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. No repository link or explicit code release statement is found. |
| Open Datasets | No | The paper is theoretical and does not conduct new experiments requiring specific public datasets. It refers to general "training dataset S = {z1, . . . , zn}" but does not provide concrete access information for any specific dataset. |
| Dataset Splits | No | The paper is theoretical and does not conduct new experiments requiring dataset splits. Therefore, it does not provide specific dataset split information. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments requiring specific hardware. Therefore, it does not provide specific hardware details used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments requiring specific software. Therefore, it does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not report on experiments. Therefore, it does not provide concrete hyperparameter values, training configurations, or system-level settings. |