Operator-adaptive PLS and Ridge models internalize preprocessing selection via linear operators and fold-local branches, achieving median RMSEP/PLS ratio of 0.960 on 57 datasets and 2.22% improvement over tuned Ridge on 52 datasets.
Kowalski , abstract =
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On-policy distillation gains efficiency from early foresight in module allocation and low-rank update directions, enabling EffOPD to accelerate training by 3x via adaptive extrapolation without extra modules or tuning.
citing papers explorer
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Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models
Operator-adaptive PLS and Ridge models internalize preprocessing selection via linear operators and fold-local branches, achieving median RMSEP/PLS ratio of 0.960 on 57 datasets and 2.22% improvement over tuned Ridge on 52 datasets.
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Learning to Foresee: Unveiling the Unlocking Efficiency of On-Policy Distillation
On-policy distillation gains efficiency from early foresight in module allocation and low-rank update directions, enabling EffOPD to accelerate training by 3x via adaptive extrapolation without extra modules or tuning.