Author

Dongjin Song

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#machine learning Preprint Oct 2026

Test-Time Compute for Tabular Foundation Models: Mechanisms, Gains, and Limits

Which forms of test-time compute improve the predictions of strong pretrained tabular foundation models (TFMs)? We systematically study this along three axes: adaptation, aggregation, and context construction. Our evaluation spans modern TFMs across the TabArena benchmark, supplemented by experiments on wide and large-...

Kang-Hui Ning, Marin Bilos, James T. Wilson et al. · 0 citations

#machine learning Preprint May 2025

HERO: Preserving Structure and Semantics in Heterogeneous Continual Graph Learning

Heterogeneous continual graph learning framework HERO is introduced, a heterogeneous continual graph learning framework that preserves historical knowledge at both structural and semantic levels while adapting to incoming tasks and aligns task-conditioned predictions and heterogeneous semantic responses through multi-l...

Guiquan Sun, Xikun Zhang, Jingchao Ni et al. · 1 citation