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New algorithm improves regret guarantees for adversarial bandits

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new algorithm for adversarial bandits that achieves near-optimal first-order regret guarantees for graphs with feedback. This algorithm bridges a gap between existing theoretical bounds, offering an expected regret bound of $\widetilde{O}(\sqrt{\alpha L^\star}+\alpha)$. The approach combines the EXP3 update method with a novel sampling rule inspired by exploration by optimization, and it can be computed efficiently through a geometrically convergent iteration. AI

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

RANK_REASON The item is an academic paper published on arXiv detailing a new algorithm and theoretical results in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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New algorithm improves regret guarantees for adversarial bandits

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The item is an academic paper published on arXiv detailing a new algorithm and theoretical results 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) · Mengxiao Zhang, Yuheng Zhang, Haipeng Luo, Paul Mineiro ·

    Near-Optimal First-Order Regret for Bandits with Feedback Graphs

    arXiv:2610.40861v1 Announce Type: cross Abstract: We study first-order regret guarantees for adversarial bandits with a fixed, known directed feedback graph. For strongly observable graphs, [ACBDK15] establishes a minimax regret of $\widetilde{\Theta}(\sqrt{\alpha T})$, where $\a…

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