Two new research papers published on arXiv propose unified frameworks for uncertainty quantification in regression tasks. The first paper introduces a family of measures based on kernel scores, offering a principled design for new uncertainty measures whose behavior is governed by kernel choice. The second paper provides an axiomatic assessment of entropy- and variance-based uncertainty measures, developing a general parametric formulation for continuous spaces. Both works aim to address the current gap in regression uncertainty quantification, which is largely overshadowed by classification-focused studies, and offer practical guidelines for practitioners. AI
IMPACT These papers offer new theoretical frameworks and practical guidelines for improving the reliability of AI models in safety-critical regression tasks.
RANK_REASON Two academic papers published on arXiv proposing new frameworks for uncertainty quantification in regression.
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- An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression
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- Uncertainty Quantification for Regression: A Unified Framework based on kernel scores
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