104 citations · 282 across the 16 of their papers we have counts for
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cs.LG2022★ 6 cited
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives
Matthias Feurer, Katharina Eggensperger, Edward Bergman +3
Modern machine learning models are often constructed taking into account multiple objectives, e.g., minimizing inference time while also maximizing accuracy. Multi-objective hyperp…
cs.LG2022★ 104 cited
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
Noah Hollmann, Samuel Müller, Katharina Eggensperger +1
We present TabPFN, a trained Transformer that can do supervised classification for small tabular datasets in less than a second, needs no hyperparameter tuning and is competitive w…