Algorithms with Calibrated Machine Learning Predictions

• URL: http://arxiv.org/abs/2502.02861v2
• Date: Thu, 06 Feb 2025 22:00:42 GMT
• Title: Algorithms with Calibrated Machine Learning Predictions
• Authors: Judy Hanwen Shen, Ellen Vitercik, Anders Wikum,
• Abstract summary: The field of algorithms with predictions incorporates machine learning advice in the design of online algorithms to improve real-world performance.<n>We propose calibration as a principled and practical tool to bridge this gap, demonstrating the benefits of calibrated advice through two case studies.<n>For ski rental, we design an algorithm that achieves optimal prediction-dependent performance and prove that, in high-variance settings, calibrated advice offers more effective guidance than alternative methods for uncertainty.<n>For job scheduling, we demonstrate that using a predictor leads to significant performance improvements over existing methods.
• Score: 9.18151868060576
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The field of algorithms with predictions incorporates machine learning advice in the design of online algorithms to improve real-world performance. While this theoretical framework often assumes uniform reliability across all predictions, modern machine learning models can now provide instance-level uncertainty estimates. In this paper, we propose calibration as a principled and practical tool to bridge this gap, demonstrating the benefits of calibrated advice through two case studies: the ski rental and online job scheduling problems. For ski rental, we design an algorithm that achieves optimal prediction-dependent performance and prove that, in high-variance settings, calibrated advice offers more effective guidance than alternative methods for uncertainty quantification. For job scheduling, we demonstrate that using a calibrated predictor leads to significant performance improvements over existing methods. Evaluations on real-world data validate our theoretical findings, highlighting the practical impact of calibration for algorithms with predictions.

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