TabArena launches a dynamic, updatable benchmarking system for tabular ML that shows boosted trees remain competitive, deep learning matches them under larger budgets with ensembling, foundation models excel on small data, and cross-model ensembles advance SOTA while flagging validation overfitting.
Gradient boosting trees and large language models for tabular data few-shot learning
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BioBERT achieved highest accuracy and strongest concordance with traditional signals on AILF and TRAM datasets, outperforming XGBoost, ALBERT, and Med-LLaMA; domain-specific pre-training proved decisive over scale.
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TabArena: A Living Benchmark for Machine Learning on Tabular Data
TabArena launches a dynamic, updatable benchmarking system for tabular ML that shows boosted trees remain competitive, deep learning matches them under larger budgets with ensembling, foundation models excel on small data, and cross-model ensembles advance SOTA while flagging validation overfitting.
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The Critical Role of Model Selection in Causal Inference: A Comparative Analysis of Classification Models within the InferBERT Framework for Pharmacovigilance
BioBERT achieved highest accuracy and strongest concordance with traditional signals on AILF and TRAM datasets, outperforming XGBoost, ALBERT, and Med-LLaMA; domain-specific pre-training proved decisive over scale.