Do Existing Preconditioners Improve Biomedical Tabular Foundation Learning? An Empirical Study on TabPFN Optimization
Tabular foundation models have recently shown strong potential for structured biomedical data analysis. Among them, TabPFN has emerged as an effective approach for low-data tabular classification tasks. However, the impact of optimization and preconditioning strategies on biomedical fine-tuning remains largely unexplored. In this work, we present a comprehensive empirical investigation of five AdamW-based preconditioning strategies for fine-tuning TabPFN v2.5 on 59 biomedical datasets spanning Alzheimer's disease, breast cancer, schizophrenia, significant memory concern (SMC), KEEL biomedical datasets, and UCI biomedical benchmarks. The evaluation considers predictive performance, computa...
| Authors | M. Sajid, Pinki Khatun, M. Tanveer |
|---|---|
| Primary category | cs.AI |
| Published | 1 August 2026 |
| Last updated at source | 1 August 2026 |
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