New research shows exact truthfulness incompatible with sequential prediction calibration

A new paper by Haghtalab et al. explores truthful calibration measures for sequential prediction, building on prior work from 2024. The researchers demonstrate that exact truthfulness in calibration measures is incompatible with completeness and soundness for sequential binary prediction, even with independent outcomes. They then propose two general reductions to create approximately truthful calibration measures, with one method improving the approximate-truthfulness guarantee of previous research. AI

IMPACT This research contributes to the theoretical understanding of probabilistic forecasting and calibration in AI systems.

RANK_REASON The cluster contains a new academic paper detailing theoretical research findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• Bibliographic Explorer
• CatalyzeX Code Finder for Papers
• Connected Papers
• CORE Recommender
• DagsHub
• Gotit.pub
• Haghtalab et al.
• Hugging Face
• Influence Flower
• Litmaps
• ScienceCast
• scite Smart Citations

• paper
• other

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