End-to-End Conformal Calibration for Optimization Under Uncertainty

TMLR Paper5153 Authors

Abstract: Machine learning can significantly improve performance for decision-making under uncertainty in a wide range of domains. However, ensuring robustness guarantees requires well-calibrated uncertainty estimates, which can be difficult to achieve with neural networks. Moreover, in high-dimensional settings, there may be many valid uncertainty estimates, each with their own performance profile—_i.e._, not all uncertainty is equally valuable for downstream decision-making. To address this problem, this paper develops an end-to-end framework to _learn_ uncertainty sets for conditional robust optimization in a way that is informed by the downstream decision-making loss, with robustness and calibration guarantees provided by conformal prediction. In addition, we propose to represent general convex uncertainty sets with partially input-convex neural networks, which are learned as part of our framework. Our approach consistently improves upon two-stage estimate-then-optimize baselines on concrete applications in energy storage arbitrage and portfolio optimization.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Jake_Snell1

Submission Number: 5153