A new corpus of 108 mixed string-numeric tables shows that advanced tabular learners with basic string embeddings perform well on most real-world data, while large LLM encoders help on free-text heavy tables.
A new measure of rank correlation.Biometrika, 30(1-2):81–93
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TipPFN uses prior-data fitted networks and in-context learning on synthetic bifurcation data to detect proximity to critical transitions in unseen dynamical systems and real observations.
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citing papers explorer
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STRABLE: Benchmarking Tabular Machine Learning with Strings
A new corpus of 108 mixed string-numeric tables shows that advanced tabular learners with basic string embeddings perform well on most real-world data, while large LLM encoders help on free-text heavy tables.
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In-context learning to predict critical transitions in dynamical systems
TipPFN uses prior-data fitted networks and in-context learning on synthetic bifurcation data to detect proximity to critical transitions in unseen dynamical systems and real observations.
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ModelLens: Finding the Best for Your Task from Myriads of Models
ModelLens learns a performance-aware latent space from 1.62M leaderboard records to rank unseen models on unseen datasets without forward passes on the target.