This paper introduces a new approach to online interval selection, focusing on binary predictions for membership in an optimal solution. The proposed method achieves a consistency bound of $d=(3+\sqrt5)/2$ under finite strict robustness, with a deterministic algorithm that attains this bound with exact robustness. The work also explores a tunable replacement cost that offers a continuous family of solutions with exact consistency and robustness on specific parameter intervals. Randomization is discussed as a means to approach expected consistency of one with finite expected robustness, though almost-sure robustness requires expected consistency strictly above two. AI
IMPACT Introduces theoretical advancements in online decision-making with binary predictions, potentially impacting AI systems that require sequential decision-making under uncertainty.
RANK_REASON The item is a research paper detailing a new algorithm and theoretical analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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