OpenML
PulseAugur coverage of OpenML — every cluster mentioning OpenML across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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TRL-Bench standardizes tabular encoder evaluation
Researchers have introduced TRL-Bench, a new benchmark designed to standardize the evaluation of tabular encoders across different training paradigms. This benchmark allows for direct comparison of models by exporting r…
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New method explains outlier detection for Local Outlier Factor
Researchers have developed a new method called Density-Based Counterfactuals for Outliers (DCFO) to explain why certain data points are identified as outliers by the Local Outlier Factor (LOF) algorithm. DCFO partitions…
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New studies probe tabular foundation model mechanisms and ensembling
Two new research papers delve into the intricacies of tabular foundation models (TFMs), exploring their performance and ensemble strategies. The first paper provides a mechanistic study, analyzing how different TFM arch…
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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 …