@inproceedings{
anonymous2023mitigating,
title={Mitigating Tail Catastrophe in Steered Database Query Optimization with Risk-Averse Contextual Bandits},
author={Anonymous},
booktitle={Machine Learning for Systems 2023},
year={2023},
url={https://openreview.net/forum?id=gxnmqD6tGy}
}
Contextual bandits with average-case statistical guarantees are inadequate in risk-averse situations because they might trade off degraded worst-case behaviour for better average performance. Designing a risk-averse contextual bandit is challenging because exploration is necessary but risk-aversion is sensitive to the entire distribution of rewards; nonetheless we exhibit the first risk-averse contextual bandit algorithm with an online regret guarantee. We apply the technique to a self-tuning software scenario in a production exascale data processing system, where worst-case outcomes should be avoided.
Mitigating Tail Catastrophe in Steered Database Query Optimization with Risk-Averse Contextual Bandits