8 papers
Online Smoothed Demand Management
Adam Lechowicz, Nicolas Christianson, Mohammad Hajiesmaili +2
We introduce and study a class of online problems called online smoothed demand management , motivated by paradigm shifts in grid integration and energy storage fo…
End-to-End Conformal Calibration for Optimization Under Uncertainty
Christopher Yeh, Nicolas Christianson, Alan Wu +2
Machine learning can significantly improve performance for decision-making under uncertainty across a wide range of domains. However, ensuring robustness guarantees requires well-c…
Conformal Risk Training: End-to-End Optimization of Conformal Risk Control
Christopher Yeh, Nicolas Christianson, Adam Wierman +1
While deep learning models often achieve high predictive accuracy, their predictions typically do not come with any provable guarantees on risk or reliability, which are critical f…
Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraints
Adam Lechowicz, Nicolas Christianson, Bo Sun +4
We introduce and study spatiotemporal online allocation with deadline constraints (), a new online problem motivated by emerging challenges in sustainability and ene…
Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms
Adam Lechowicz, Nicolas Christianson, Bo Sun +4
We introduce and study online conversion with switching costs, a family of online problems that capture emerging problems at the intersection of energy and sustainability. In this…
Chasing Convex Functions with Long-term Constraints
Adam Lechowicz, Nicolas Christianson, Bo Sun +4
We introduce and study a family of online metric problems with long-term constraints. In these problems, an online player makes decisions in a metric space t…