Mu-Shan Li

From matrix inversion to constraints: provably tighter confidence regions for importance weights in label shift

This work proposes a paradigm shift from inversion-based inference to a direct matrix constraint framework, which is used to define a joint confidence region and extract marginal intervals via linear programming, deriving provably tighter bounds for importance weights while maintaining exact finite-sample validity.