Online Resource Allocation with Convex-set Machine-Learned Advice

Understand

Decision-makers often have access to machine-learned predictions about future demand that can help guide online resource allocation decisions.

• However, such predictions may be inaccurate.
• We develop a framework for online resource allocation with potentially unreliable machine-learned advice, where the advice is represented as a convex uncertainty set for the demand vector rather than a single point estimate.
• We introduce a parameterized class of Pareto-optimal online algorithms that balance consistency and robustness.