This paper introduces new algorithms for online aggregation with delays on a line metric, focusing on learning-augmented and randomized approaches. The authors propose a deterministic learning-augmented algorithm called Balance, offering specific robustness and consistency guarantees. Additionally, a randomized algorithm is presented that achieves a competitive ratio of e+1 against an oblivious adversary, improving upon existing deterministic benchmarks and establishing a new lower bound for randomized online algorithms. The research also combines these techniques to develop a randomized learning-augmented algorithm with enhanced robustness and consistency. AI
RANK_REASON The cluster contains an academic paper detailing new algorithms and theoretical analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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