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New PPLS framework offers calibrated uncertainty and improved accuracy

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework for Probabilistic Partial Least Squares (PPLS) that addresses practical limitations in existing fitting pipelines. This framework combines noise pre-estimation, constrained likelihood optimization, and prediction calibration, offering an end-to-end solution. The method utilizes exact Stiefel-manifold optimization and noise-subspace estimation, achieving improved accuracy and calibrated uncertainty across various benchmarks, including multi-omics datasets. AI

IMPACT Introduces a novel statistical method for two-view learning, potentially improving accuracy and uncertainty calibration in multi-omics data analysis.

RANK_REASON The cluster contains an academic paper detailing a new statistical method and its evaluation on benchmarks.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New PPLS framework offers calibrated uncertainty and improved accuracy

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Haoran Hu, Xingce Wang ·

    Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty

    arXiv:2605.11607v1 Announce Type: new 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 Bouha…

  2. arXiv stat.ML TIER_1 English(EN) · Xingce Wang ·

    Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty

    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…

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