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New research paper details algorithmic predictor refinement techniques

By PulseAugur Editorial · [1 sources] ·

A new research paper titled "Refinement as a Service: Algorithmic Predictor Refinement" explores the concept of prediction aggregation, specifically focusing on calibrated predictors. The paper introduces a formulation where calibrated predictors are viewed as signaling schemes and refinement is defined through feature-independent garblings. The authors establish an algorithmic framework, demonstrating that while bilateral refinement can be solved efficiently, refinement with multiple predictors becomes NP-hard for deterministic outputs. However, when randomized outputs are permitted, a polynomial-time algorithm is presented for any number of input predictors by decomposing constructible signal vectors. AI

RANK_REASON The cluster contains a single item detailing a new academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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New research paper details algorithmic predictor refinement techniques

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The cluster contains a single item detailing a new academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Wei Tang, Hanrui Zhang ·

    Refinement as a Service: Algorithmic Predictor Refinement

    arXiv:2610.11415v1 Announce Type: cross Abstract: Prediction aggregation aims to combine information from multiple predictors into a more informative one. We study this question in the setting of calibrated predictors, where each prediction must equal the conditional expectation …

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