Retrieval & Fine-Tuning for In-Context Tabular Models
<|reference_start|>Retrieval & Fine-Tuning for In-Context Tabular Models: Tabular data is a pervasive modality spanning a wide range of domains, and the inherent diversity poses a considerable challenge for deep learning. Recent advancements using transformer-based in-context learning have shown promise on smaller and less complex datasets, but have struggled to scale to larger and more complex ones. To address this limitation, we propose a combination of retrieval and fine-tuning: we can adapt the transformer to a local subset of the data by collecting nearest neighbours, and then perform task-specific fine-tuning with this retrieved set of neighbours in context. Using TabPFN as the base model -- currently the best tabular in-context learner -- and applying our retrieval and fine-tuning scheme on top results in what we call a locally-calibrated PFN, or LoCalPFN. We conduct extensive evaluation on 95 datasets curated by TabZilla from OpenML, upon which we establish a new state-of-the-art with LoCalPFN -- even with respect to tuned tree-based models. Notably, we show a significant boost in performance compared to the base in-context model, demonstrating the efficacy of our approach and advancing the frontier of deep learning in tabular data.<|reference_end|>
@article{thomas2024retrieval,
    title={Retrieval & Fine-Tuning for In-Context Tabular Models},
    author={Valentin Thomas, Junwei Ma, Rasa Hosseinzadeh, Keyvan Golestan,
  Guangwei Yu, Maksims Volkovs, Anthony Caterini},
    journal={arXiv preprint arXiv:2406.05207},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05207},
    primaryClass={cs.LG}
}