Automated Machine Learning for Unsupervised Tabular Tasks - Research article overview

Aug 2, 2026

This paper introduces LOTUS (Learning to Learn with Optimal Transport for Unsupervised Scenarios), a method for model selection across multiple unlabeled tabular machine learning tasks such as outlier detection and clustering. LOTUS estimates similarity between new and prior datasets by using Optimal Transport distances derived from unlabeled data, then recommends machine learning pipelines using a single unified approach. Experiments against strong baselines demonstrate the effectiveness of the proposed strategy and its potential as an initial step toward automated model selection for unsupervised scenarios.

LanguageEnglish

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Pages24

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Introduction

AutoML for Unsupervised tasks

Automated Machine Learning for Unsupervised Tabular Tasks Citation for published version (APA): Singh, P. , Gijsbers, P. , Yildirim, E. C. G. , Yildirim, M. O. , & Vanschoren, J. (2025) . Automated Machine Learning [for Unsupervised Tabular Tasks](for Unsupervised Tabular Tasks. arXiv.org)[. arXiv.org](for Unsupervised Tabular Tasks. arXiv.org). [https://doi.org/10.48550/arXiv.2510.07569](https://doi.org/10.48550/arXiv.2510.07569) DOI: 10.48550/arXiv.2510.07569 Document status and date: Published: 08/10/2025 Document Version: Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement: [www.tue.nl/taverne](www.tue.nl/taverne) Take down policy If you believe that this document breaches copyright please contact us at: [openaccess@tue.nl](openaccess@tue.nl) providing details and we will investigate your claim. Download date: 26. Apr. 2026 arXiv :2510 .07569v 1 [ cs .LG] 8 Oct 2025 Automated Machine Learning for Unsupervised Tabular Tasks Prabhant Singh 1*, Pieter Gijsbers 1 , Elif Ceren Gok Yildirim 1 , Murat Onur Yildirim 1 , Joaquin Vanschoren 1 1AMOR/e Lab, Eindhoven University of Technology, Eindhoven, 5600 MB, Netherlands. *Corresponding author(s). E-mail(s): [p.singh@tue.nl](p.singh@tue.nl) ; In this work, we present LOTUS (Learning to Learn with Optimal Transport for Unsupervised Scenarios), a simple yet effective method to perform model selection for multiple unsupervised machine learning(ML) tasks such as outlier detection and clustering. Our intuition behind this work is that a machine learning pipeline will perform well in a new dataset if it previously worked well on datasets with a similar underlying data distribution . We use Optimal Transport distances to find this similarity between unlabeled tabular datasets and recommend machine learning pipelines with one unified single method on two downstream unsupervised tasks: outlier detection and clustering. We present the effectiveness of our approach with experiments against strong baselines and show that LOTUS is a very promising first step toward modelselection for multiple unsupervised ML tasks.1 1 Introduction Automated Machine Learning (AutoML) [1] aims to automate the design and optimization of machine learning pipelines in a data-driven way, using a variety of optimization techniques to find the best pipeline in a vast search space of possible pipelines consisting of many data preparation steps and modeling techniques. AutoML has shown p

FAQs

What problem does LOTUS address in unsupervised machine learning?

LOTUS targets model selection for multiple unsupervised tabular tasks, where ground-truth labels are unavailable and AutoML search lacks reliable evaluation metrics.

How does LOTUS measure similarity between datasets?

LOTUS uses Optimal Transport distances to quantify similarity between unlabeled tabular datasets, based on their underlying data distributions.

Which downstream unsupervised tasks does the method apply to?

The paper applies LOTUS to outlier detection and clustering, recommending machine learning pipelines through a unified single method.

Aug 2, 2026

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