2 papers
cs.LG2026
Beyond Foundation Models: PFN Priors Improve Tabular Hyperparameter Optimization
Artem Chistyakov, Renat Sergazinov, Artem Babenko
Synthetic priors have so far served one purpose: pretraining tabular foundation models. We ask whether the same priors can improve conventional tabular models, which are trained fr…
cs.LG2026
Agentic Search Spaces for Tabular Machine Learning
Renat Sergazinov, Artem Chistyakov, Sergey Pankevich +1
Despite the rapid progress of LLM-based agents for planning, code generation, and debugging, their practical value for tabular machine learning remains underexplored. In this paper…