Viktor Bengs
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126
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ML×1AI×1Stats ML×1
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2026
Calibrated Preference Learning: The Case of Label Ranking
The paper formalizes the concept of calibration for probabilistic label ranking, demonstrating that popular models are often poorly calibrated and that calibration captures a meaningful quality dimension beyond simple top-1 accuracy.
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