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This paper investigates the challenges posed by unreliable advice in learning-augmented online allocation problems, focusing on efficiency and fairness. The authors propose a novel algorithm that integrates conservative fallback strategies with fairness corrections, ensuring robust decision-making even in the presence of adversarial advice. Experimental results demonstrate that their approach not only maintains consistency under bounded-error assumptions but also significantly reduces exposure disparity among candidates.
Unreliable advice can lead to significant inefficiencies, but a new robust algorithm shows how to mitigate these risks while enhancing fairness in online allocation.
Learning-augmented algorithms improve online decisions using predictions, but unreliable advice may harm efficiency and fairness. We study an online allocation problem with finite candidate sets, irreversible decisions, and exposure constraints. We propose a robust and fair rule combining advice with a conservative fallback and fairness correction. Under bounded-error assumptions, we prove consistency and robustness with loss proportional to prediction error. Experiments show stability under adversarial advice and significant reductions in exposure disparity.
