@inproceedings{
zhang2024hypertime,
title={HyperTime: Hyperparameter Optimization for Combating Temporal Distribution Shifts},
author={Shaokun Zhang and Yiran Wu and Zhonghua Zheng and Qingyun Wu and Chi Wang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=XtnTav9T4g}
}
In this work, we propose a hyperparameter optimization method named HyperTime to find hyperparameters robust to potential temporal distribution shifts in the unseen test data. Our work is motivated by an important observation that it is, in many cases, possible to achieve temporally robust predictive performance via hyperparameter optimization. Based on this observation, we leverage the ‘worst-case-oriented’ philosophy from the robust optimization literature to help find such robust hyperparameter configurations. HyperTime imposes a lexicographic priority order on average validation loss and worst-case validation loss over chronological validation sets. We perform a theoretical analysis on the upper bound of the expected test loss, which reveals the unique advantages of our approach. We also demonstrate the strong empirical performance of the proposed method on multiple machine learning tasks with temporal distribution shifts.
HyperTime: Hyperparameter Optimization for Combating Temporal Distribution Shifts
Shaokun Zhang Yiran Wu Zhonghua Zheng Qingyun Wu Chi Wang