Introduces Clipped Linear Lottery with linear probability scaling between thresholds, proving its worst-case regret nearly matches the lower bound for any smooth selection rule and showing improved stability on real peer-review datasets.
It effectively requires the funder to relax their ex post validity constraint
1 Pith paper cite this work. Polarity classification is still indexing.
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Smooth Partial Lotteries for Stable Randomized Selection
Introduces Clipped Linear Lottery with linear probability scaling between thresholds, proving its worst-case regret nearly matches the lower bound for any smooth selection rule and showing improved stability on real peer-review datasets.