Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty
arXiv:2605.11607v2 Announce Type: replace-cross Abstract: Probabilistic partial least squares (PPLS) is a central likelihood-based model for two-view learning when one needs both interpretable latent factors and calibrated uncertainty. Building on the identifiable parameterization of Bouhaddani et al.\ (2018), existing fitting pipelines still face two practical bottlenecks: noise--signal coupling under joint EM/ECM updates and nontrivial handling of orthogonality constraints. Following the fixed-noise scalar-likelihood protocol, we develop an end-to-end framework that combines noise pre-estima
This academic paper, published in 2026, represents incremental progress in advanced statistical methods for machine learning, building on existing research.
While contributing to the theoretical underpinnings of AI, this specific research is highly specialized and unlikely to have immediate strategic implications for a broad audience.
It offers refined algorithms and error bounds for a specific probabilistic model, potentially improving the accuracy and interpretability of certain machine learning applications in the long term.
Improved performance for specific scientific and statistical modeling tasks using probabilistic partial least squares.
Potentially enables more robust and reliable data analysis in niche AI applications where interpretability and uncertainty quantification are critical.
Could contribute to the broader academic advancement of interpretable AI, but far removed from commercial or geopolitical impact.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG