6 papers
Explaining Tabular Foundation Model Differences Through Meta-Features
Markus Herre, Andrej Tschalzev, Sascha Marton +1
With the rise of tabular foundation models alongside traditional models still performing well on many tasks, choosing the right model for a tabular dataset remains difficult. We in…
Beyond IID: How General Are Tabular Foundation Models, Really?
Lennart Purucker, Andrej Tschalzev, Nick Erickson +7
Foundation models for predictive machine learning on tabular data have recently gained significant traction in academia and industry. Research communities across disciplines are in…
TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
Andrej Tschalzev, Nick Erickson, Yuyang Wang +4
Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures. At the same time, feature engineering remains a critical yet underexplor…
TabArena: A Living Benchmark for Machine Learning on Tabular Data
Nick Erickson, Lennart Purucker, Andrej Tschalzev +4
With the growing popularity of deep learning and foundation models for tabular data, the need for standardized and reliable benchmarks is higher than ever. However, current benchma…
Unreflected Use of Tabular Data Repositories Can Undermine Research Quality
Andrej Tschalzev, Lennart Purucker, Stefan Lüdtke +3
Data repositories have accumulated a large number of tabular datasets from various domains. Machine Learning researchers are actively using these datasets to evaluate novel approac…
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data
Andrej Tschalzev, Sascha Marton, Stefan Lüdtke +2
Tabular data is prevalent in real-world machine learning applications, and new models for supervised learning of tabular data are frequently proposed. Comparative studies assessing…