Summary of Clams: a System For Zero-shot Model Selection For Clustering, by Prabhant Singh et al.
CLAMS: A System for Zero-Shot Model Selection for Clustering
by Prabhant Singh, Pieter Gijsbers, Murat Onur Yildirim, Elif Ceren Gok, Joaquin Vanschoren
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers develop an Automated Machine Learning (AutoML) system capable of selecting the most suitable algorithms for clustering problems. The proposed system uses optimal transport-based dataset similarity to identify the best model for a given task. The objective is to establish a comprehensive pipeline for AutoML in clustering applications and provide recommendations for selecting the most effective algorithms. Experimental results show that the proposed approach outperforms multiple clustering baselines, demonstrating its utility in solving clustering problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For curious high school students or non-technical adults, this paper is about creating an automated system to help machines learn from data without needing human expertise. The goal is to make it easier for machines to pick the best way to group similar things together (like grouping animals by their characteristics). The researchers tested their system against other methods and found that it works better, which could lead to new ways of solving problems in areas like computer vision or natural language processing. |
Keywords
* Artificial intelligence * Clustering * Machine learning * Natural language processing
