Exact Stiefel optimization and noise-subspace estimation yield closed-form updates, finite-sample error bounds, and near-nominal coverage for probabilistic PLS without post-hoc recalibration.
Since ˆθN minimizesL, η≤ L ∞(ˆθN)− L ∞(θ0)≤2 sup θ∈K0 |L(θ)− L ∞(θ)|, so Pr( ˆθN ∈K ε)→0
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Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty
Exact Stiefel optimization and noise-subspace estimation yield closed-form updates, finite-sample error bounds, and near-nominal coverage for probabilistic PLS without post-hoc recalibration.