OpenML
PulseAugur coverage of OpenML — every cluster mentioning OpenML across labs, papers, and developer communities, ranked by signal.
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New method tackles label shift in tabular foundation models
Researchers have introduced DistPFN, a novel method to address label shift in tabular foundation models like TabPFN. This technique adjusts predictions at test time by re-weighting the influence of training data's class…
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PUICL transformer enables in-context positive-unlabeled learning without fitting
Researchers have developed PUICL, a pretrained transformer model capable of performing positive-unlabeled (PU) learning through in-context learning. This approach eliminates the need for dataset-specific training or ite…
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ScoringBench: A Benchmark for Evaluating Tabular Foundation Models with Proper Scoring Rules
Two new research papers introduce methods for better evaluating and cleaning tabular foundation models. ScoringBench offers a comprehensive benchmark using proper scoring rules to assess model performance beyond simple …