Model Assessment and Selection under Temporal Distribution Shift

Paper: Han, E., Huang, C. and Wang, K., 2024. Model Assessment and Selection under Temporal Distribution Shift. arXiv preprint arXiv:2402.08672. To appear in ICML 2024.

Python implementations

The file ARW.py provides Python implementations of Adaptive Rolling Window (ARW) methods for model assessment and selection.

1. Function ARWME: Adaptive Rolling Window for Mean Estimation (Algorithm 1 in the paper).

2. Function tournament_selection: Single-Elimination Tournament for Model Selection (Algorithm 3 in the paper).

Experiments in the paper

The folders code-synthetic-data, code-arxiv, and code-housing contain all the code for reproducing the experimental results.

Citation

@article{HHW24,
  title={Model Assessment and Selection under Temporal Distribution Shift},
  author={Han, Elise and Huang, Chengpiao and Wang, Kaizheng},
  journal={arXiv preprint arXiv:2402.08672},
  year={2024}
}