Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Sharper Generalization Bounds for Pairwise Learning
Authors: Yunwen Lei, Antoine Ledent, Marius Kloft
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we provide a reο¬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. |