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 Clustering
Authors: Shaojie Li, Yong Liu
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose a unified clustering learning framework and investigate its excess risk bounds, obtaining state-of-the-art upper bounds under mild assumptions. Specifically, we derive sharper bounds of order O(K2/n) under mild assumptions on the covering number of the hypothesis spaces, where these assumptions are easy to be verified. Moreover, for the hard clustering scheme, such as (kernel) k-means, if just assume the hypothesis functions to be bounded, we improve the upper bounds from the order O(K/ n) to O( K/ n). Furthermore, state-of-the-art bounds of faster order O(K/n) are obtained with the covering number assumptions. |
| Researcher Affiliation | Academia | 1Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China 2Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments or use datasets, so no information about public datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, thus no training/validation/test dataset splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not conduct experiments, thus no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not conduct experiments, thus no experimental setup details like hyperparameters or training settings are provided. |