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New framework reframes Learning to Defer via density-ratio estimation

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced a novel post-hoc Learning to Defer (L2D) framework that reframes the problem through the lens of ideal distributions. This approach defines deferral by calculating the density-ratio between a model's and an expert's ideal distributions. The derived DR CPE losses allow for adjustable deferral rates without the need for retraining, and experimental results show competitive performance and robustness across various datasets. AI

IMPACT Introduces a new theoretical framework for model deferral, potentially improving system reliability and interpretability.

RANK_REASON The cluster contains an academic paper detailing a new methodology for machine learning.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework reframes Learning to Defer via density-ratio estimation

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Alexander Soen, Ragnar Thobaben, Joakim Jald\'en, Richard Nock ·

    Density-Ratio Losses for Post-Hoc Learning to Defer

    arXiv:2605.19557v1 Announce Type: new Abstract: We study post-hoc Learning to Defer (L2D) through the lens of ideal distributions: divergence-regularized reweightings of the data distribution under which a model attains low loss. We define deferral via the density-ratio between a…

  2. arXiv stat.ML TIER_1 English(EN) · Richard Nock ·

    Density-Ratio Losses for Post-Hoc Learning to Defer

    We study post-hoc Learning to Defer (L2D) through the lens of ideal distributions: divergence-regularized reweightings of the data distribution under which a model attains low loss. We define deferral via the density-ratio between a model's and an expert's ideals. Using the reduc…

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