2 citations · 3 across the 8 of their papers we have counts for
4 papers · 1 filter
Lost in Aggregation: How Benchmarks Overlook Irreplaceable Model Strengths
Andrej Tschalzev, Stefan Lüdtke, Heiner Stuckenschmidt +1
Tabular machine learning benchmarks typically summarize performance by averaging scores, ranks, or pairwise wins across datasets. Such aggregates are useful for selecting robust de…
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…