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New framework enhances sequential learning with partial feedback

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for sequential learning with partial feedback, utilizing the Estimation-to-Decisions (E2D) meta-algorithm. This approach employs an online regression oracle to efficiently estimate outcome distributions based on historical data. The study proposes two computationally efficient exploration rules, one based on epsilon-greedy and the other on inverse gap weighting, to achieve optimal regret orders. The method was evaluated on a synthetic label-efficient prediction game and an LLM routing task, demonstrating superior performance compared to existing methods. AI

IMPACT This research could improve the efficiency of LLM routing and other sequential learning tasks with partial feedback.

RANK_REASON The cluster contains an academic paper detailing a new algorithm and its evaluation. [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 framework enhances sequential learning with partial feedback

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12 / 100
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The cluster contains an academic paper detailing a new algorithm and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Kapilan Balagopalan, Hao Qin, Kwang-Sung Jun, Chicheng Zhang ·

    Efficient contextual partial monitoring with general function approximation

    arXiv:2610.19804v1 Announce Type: new Abstract: We study finite stochastic partial monitoring (PM) games with side information, a paradigm of sequential learning with partial feedback. On each round $t \in [T]$, both player and environment observe a context and player chooses an …

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