Recent work in tabular machine learning has emphasized the importance of diverse and realistic evaluation settings. The niche of large-scale (1M+) data, however, remains under-explored. To study training-data scaling, we assemble eight real-world tabular datasets with up to around ten million training examples, retaini...
Ivan Rubachev, Н. А. Лазарев, Artem Babenko· 0 citations
This study suggests that LLM agents can provide practical value for tabular ML by expanding the design space, and suggests that the two strongest agentic ensembles surpass the best AutoGluon ensemble of conventional models.
Renat Sergazinov, Artem Chistyakov, Sergey Pankevich et al.· 0 citations
This work introduces TabPack, an efficient MLP ensemble with strong out-of-the-box performance and reduced reliance on traditional tuning, and specifies ranges from which to sample MLP hyperparameter rather than exact hyperparameter values, which naturally demands less precision for good performance.
Yury Gorishniy, Akim Kotelnikov, Ivan Rubachev et al.· arXiv.org· 0 citations
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