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cs.LG2026
Do You Even Need More Data? Rethinking LLM Deployment for Label-Scarce Tabular Classification with TabLens
Daria Grushina, Kseniia Kuvshinova, Alina Kostromina +3
Every tabular classification task requires sufficient labeled data. When labels are scarce, practitioners can collect more examples, encode domain expertise into features, or accep…
cs.LG2025
LightAutoDS-Tab: Multi-AutoML Agentic System for Tabular Data
Aleksey Lapin, Igor Hromov, Stanislav Chumakov +4
AutoML has advanced in handling complex tasks using the integration of LLMs, yet its efficiency remains limited by dependence on specific underlying tools. In this paper, we introd…