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New method improves multi-dimensional forecast calibration

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for online calibration of multi-dimensional forecasts over arbitrary convex sets. This approach connects calibration to external regret minimization in online linear optimization. The algorithm, which is identical across various settings, utilizes a swap regret minimization technique with the TreeSwap algorithm and Follow-The-Leader subroutine. AI

RANK_REASON The cluster contains a research paper published on arXiv detailing a new algorithm for forecast calibration. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method improves multi-dimensional forecast calibration

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The cluster contains a research paper published on arXiv detailing a new algorithm for forecast calibration. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Maxwell Fishelson, Noah Golowich, Mehryar Mohri, Jon Schneider ·

    High-Dimensional Calibration from Swap Regret

    arXiv:2505.21460v2 Announce Type: replace-cross Abstract: We study online calibration of multi-dimensional forecasts over an arbitrary convex set $P \subset \mathbb{R}^d$ relative to an arbitrary norm $|\cdot|$. We connect this to external regret minimization for online linear op…

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