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.
Mapping back through the chart diffeomorphism yields the stated limit for ˆθN [van der Vaart, 1998, Newey and McFadden, 1994, Theorem 5.39]
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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.