Exact Stiefel optimization and noise-subspace estimation yield closed-form updates, finite-sample error bounds matching a minimax rate, and native calibrated uncertainty for probabilistic PLS.
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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 matching a minimax rate, and native calibrated uncertainty for probabilistic PLS.