23 citations · 23 across the 4 of their papers we have counts for
4 papers
Automated Machine Learning for Unsupervised Tabular Tasks
Prabhant Singh, Pieter Gijsbers, Elif Ceren Gok Yildirim +2
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 unsuperv…
AutoML Benchmark with shorter time constraints and early stopping
Israel Campero Jurado, Pieter Gijsbers, Joaquin Vanschoren
Automated Machine Learning (AutoML) automatically builds machine learning (ML) models on data. The de facto standard for evaluating new AutoML frameworks for tabular data is the Au…
CLAMS: A System for Zero-Shot Model Selection for Clustering
Prabhant Singh, Pieter Gijsbers, Murat Onur Yildirim +2
We propose an AutoML system that enables model selection on clustering problems by leveraging optimal transport-based dataset similarity. Our objective is to establish a comprehens…
OpenML Benchmarking Suites
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer +6
Machine learning research depends on objectively interpretable, comparable, and reproducible algorithm benchmarks. We advocate the use of curated, comprehensive suites of machine l…