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New procedure optimizes Condorcet winner identification in dueling bandits

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new procedure for identifying the Condorcet winner in large-scale stochastic dueling bandits. This method improves upon existing techniques by utilizing the full gap matrix, which includes information from all pairwise comparisons, rather than just those involving the winner. The procedure achieves high-probability, instance-dependent sample-complexity guarantees that are shown to be optimal, revealing a general form of sampling complexity that balances locating informative comparisons with verification costs. AI

IMPACT This research advances theoretical understanding in bandit algorithms, potentially leading to more efficient AI systems for decision-making in complex environments.

RANK_REASON The cluster contains an academic paper detailing a new procedure and theoretical results in a specific area of machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

New procedure optimizes Condorcet winner identification in dueling bandits

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The cluster contains an academic paper detailing a new procedure and theoretical results in a specific area of machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · El Mehdi Saad (CC-UM6P-Rabat, UM6P), Victor Thuot (MISTEA), Nicolas Verzelen (MISTEA) ·

    The Sampling Complexity of Condorcet Winner Identification in Dueling Bandits

    arXiv:2603.15189v2 Announce Type: replace-cross Abstract: We study best-arm identification in large-scale stochastic dueling bandits under the sole assumption that a Condorcet winner exists, i.e., an arm that wins each noisy pairwise comparison with probability at least $1/2$. We…

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