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New theory achieves logarithmic high-probability regret in online convex optimization

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new theoretical framework for online convex optimization (OCO) that achieves logarithmic high-probability regret. This advancement addresses the challenge of learning with limited feedback, where only two function evaluations are available per step. The proposed method offers a significant improvement over previous analyses, particularly in its linear dependence on dimensionality compared to quadratic terms in earlier work, while maintaining logarithmic dependence on the number of iterations. AI

IMPACT This theoretical advancement in online convex optimization could lead to more efficient learning algorithms in AI systems that operate with limited feedback.

RANK_REASON The cluster contains a research paper published on arXiv detailing theoretical advancements in online convex optimization. [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 theory achieves logarithmic high-probability regret in online convex optimization

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The cluster contains a research paper published on arXiv detailing theoretical advancements in online convex optimization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Haishan Ye ·

    Logarithmic High-Probability Regret for Online Convex Optimization with Two-Point Bandit Feedback

    arXiv:2603.25029v4 Announce Type: replace Abstract: We study online convex optimization (OCO) with two-point bandit feedback against a non-anticipating adaptive adversary. In this setting, a learner competes with an adversarial sequence of convex losses while observing each loss …

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