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New algorithms tackle online fair division with limited item copies

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new algorithms for online fair division problems, addressing scenarios with numerous indivisible items and few copies. These algorithms model the problem as a contextual bandit problem, aiming to balance fairness and efficiency by learning utility functions based on item-agent features. The proposed methods achieve provable sublinear regret and have demonstrated effectiveness in experimental results. AI

IMPACT Introduces novel algorithmic approaches for resource allocation problems that could be applied in AI systems managing user-item assignments.

RANK_REASON This is a research paper detailing new algorithms for a specific problem in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New algorithms tackle online fair division with limited item copies

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This is a research paper detailing new algorithms for a specific problem in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Arun Verma, Indrajit Saha, Makoto Yokoo, Bryan Kian Hsiang Low ·

    Keep Everyone Happy: Online Fair Division of Numerous Items with Few Copies

    arXiv:2408.12845v3 Announce Type: replace-cross Abstract: This paper considers a novel variant of the online fair division problem involving multiple agents in which a learner sequentially observes an indivisible item that must be irrevocably allocated to one of the agents to ach…

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