Introduces Relative Geometric Conflict (RGC) using gradient direction comparison and empirical Fisher trace factorization to improve reliability estimation beyond loss or confidence signals in noisy-label training.
Horn and Charles R
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Recursive multi-fidelity GP regression with EM optimization trains faster than the coupled non-recursive Kennedy-O'Hagan approach on noisy non-nested data while delivering comparable predictions and uncertainty estimates.
A review summarizing definitions, canonical forms, exact and approximate distributions, numerical methods, applications, and open problems for quadratic forms in real and complex Gaussian variables, including multiforms and ratios.
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Radial-Angular Geometry for Reliable Update Diagnosis in Noisy-Label Learning
Introduces Relative Geometric Conflict (RGC) using gradient direction comparison and empirical Fisher trace factorization to improve reliability estimation beyond loss or confidence signals in noisy-label training.
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Multi-fidelity Gaussian process regression for noisy outputs and non-nested experimental designs: a comparison between the recursive and non-recursive formulations
Recursive multi-fidelity GP regression with EM optimization trains faster than the coupled non-recursive Kennedy-O'Hagan approach on noisy non-nested data while delivering comparable predictions and uncertainty estimates.
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Quadratic Forms in Gaussian Random Variables Theoretical Results and Applications
A review summarizing definitions, canonical forms, exact and approximate distributions, numerical methods, applications, and open problems for quadratic forms in real and complex Gaussian variables, including multiforms and ratios.