Modern sequential decision-making systems, such as datacenter demand response and online ad allocation, operate under significant uncertainty while requiring strict adherence to safety and resource constraints. While machine learning (ML) offers transformative potential for improving average-case efficiency, its inherent brittleness to distributional shifts poses a critical barrier to deployment in mission-critical applications. This work presents a comprehensive framework for robust learning-augmented algorithms that bridge this gap. We introduce a unified methodology for calibrating untrusted ML predictions using provable expert safeguards, such as differentiable calibrators and reservation costs, to guarantee bounded worst-case costs. We extend these foundations to structured and decentralized decision spaces, developing algorithms like LOMAR for online bipartite matching and LADO for networked control that balance robust guarantees with ML-driven gains. Finally, we translate these theoretical advances into practice for socially responsible AI, demonstrating how equity-aware geographical load balancing (eGLB) and fair schedulers (LCF) can mitigate regional environmental disparities in AI operations.
P. Jonathan Li (Fri,) studied this question.