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New research explores adversarial bandit optimization with bounded perturbations and distributed agents

By PulseAugur Editorial · [3 sources] ·

Two new research papers explore adversarial bandit optimization, a machine learning technique where losses can be non-convex and non-smooth. The first paper introduces a framework for globally budgeted perturbations to convex and beta-smooth losses, establishing expected regret guarantees. The second paper addresses distributed adversarial bandits, proposing a black-box approach that allows agents to minimize global average loss through gossip communication, achieving near-optimal regret bounds. AI

IMPACT These papers advance theoretical understanding in reinforcement learning, potentially leading to more robust and efficient decision-making algorithms in complex, uncertain environments.

RANK_REASON Two academic papers published on arXiv detailing advancements in adversarial bandit optimization.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New research explores adversarial bandit optimization with bounded perturbations and distributed agents

COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Zhuoyu Cheng, Kohei Hatano, Eiji Takimoto ·

    Adversarial Bandit Optimization with Globally Bounded Perturbations to Convex Losses

    arXiv:2606.19891v1 Announce Type: new Abstract: We study adversarial bandit optimization in which the loss functions may be non-convex and non-smooth. In each round, the learner selects an action and observes only the loss incurred at that action. The loss consists of an underlyi…

  2. arXiv cs.LG TIER_1 English(EN) · Eiji Takimoto ·

    Adversarial Bandit Optimization with Globally Bounded Perturbations to Convex Losses

    We study adversarial bandit optimization in which the loss functions may be non-convex and non-smooth. In each round, the learner selects an action and observes only the loss incurred at that action. The loss consists of an underlying convex and $β$-smooth component and an advers…

  3. arXiv cs.LG TIER_1 English(EN) · Hao Qiu, Mengxiao Zhang, Nicol\`o Cesa-Bianchi ·

    Near-Optimal Regret for Distributed Adversarial Bandits: A Black-Box Approach

    arXiv:2602.06404v2 Announce Type: replace Abstract: We study distributed adversarial bandits, where $N$ agents cooperate to minimize the global average loss while observing only their own local losses. We show that the minimax regret for this problem is $\tilde{\Theta}(\sqrt{(\rh…

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