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New online learning algorithms tackle adversarial settings with two-point feedback

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new algorithms for adversarial online learning in a setting with a totally ordered action space. The focus is on a two-point feedback mechanism where the learner plays two recommendations per round and incurs the average loss. For the separated monotone problem, the proposed method achieves regret close to the full-information bound, and for the BV problem, it achieves near-optimal regret. An application in dynamic pricing demonstrates that this two-option approach matches the full information rate, up to logarithmic factors. AI

IMPACT Introduces novel algorithms for online learning with expert advice, potentially improving decision-making in areas like dynamic pricing.

RANK_REASON Academic paper published on arXiv detailing new algorithms for online 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 online learning algorithms tackle adversarial settings with two-point feedback

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Academic paper published on arXiv detailing new algorithms for online 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) · Shiliang Zuo ·

    Online Learning with Ordered Experts (via Two-point Feedback)

    arXiv:2611.07629v1 Announce Type: new Abstract: We study adversarial online learning with expert recommendations in a totally ordered action space, where the experts' recommendations may change and be reordered arbitrarily. Our focus is two-point feedback: on each round the learn…

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