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New nonparametric kernel clustering method uses bandit feedback

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced a new nonparametric kernel clustering framework designed for scenarios involving bandit feedback. This approach, called KABC, reformulates the problem as clustering arms based on their kernel mean embeddings in a reproducing kernel Hilbert space. The KABC algorithm is proven to be delta-PAC and achieves instance-dependent guarantees, adapting to an unknown signal-to-noise ratio. Empirical validation on synthetic and real-world datasets demonstrates KABC's effectiveness in distributional clustering tasks where traditional mean-based methods fall short. AI

IMPACT Introduces a novel clustering method for bandit feedback systems, potentially improving recommendation systems and adaptive trials.

RANK_REASON The cluster contains an academic paper detailing a new algorithm and its theoretical properties. [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 nonparametric kernel clustering method uses bandit feedback

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The cluster contains an academic paper detailing a new algorithm and its theoretical properties. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Victor Thuot (MISTEA, UMPA-ENSL), Sebastian Vogt (LRR-TUM), Debarghya Ghoshdastidar (LRR-TUM), Nicolas Verzelen (MISTEA) ·

    Nonparametric Kernel Clustering with Bandit Feedback

    arXiv:2601.07535v2 Announce Type: replace Abstract: Clustering with bandit feedback refers to the problem of partitioning the arms of an $N$-armed stochastic bandit according to their underlying distributions, based on noisy observations collected sequentially and adaptively. Thi…

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