penfever/tunetables-results

This repository contains the extended raw results for the TuneTables paper.

These results, and the associated metadata, are in TabZilla format; see https://github.com/naszilla/tabzilla for more explanation.

Directory Structure

tabzilla_metadata: contains the performance results for most baseline methods reported in our paper (classification only)
excelformer: contains the performance results for the excelformer baseline method
regression: contains the baseline results for regression
datasets_used: descriptions of the datasets used in the paper, including OpenML IDs
TuneTables-Hard Results: 03-2024-tt-hard-main-plotting-data
TabZilla Results: 05-2024-tabzilla-main-plotting-data

If you find this repository useful, please consider citing our paper.

@misc{feuer2024tunetablescontextoptimizationscalable,
      title={TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks}, 
      author={Benjamin Feuer and Robin Tibor Schirrmeister and Valeriia Cherepanova and Chinmay Hegde and Frank Hutter and Micah Goldblum and Niv Cohen and Colin White},
      year={2024},
      eprint={2402.11137},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2402.11137}, 
}