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Explores robust regression in genomic data analysis, focusing on downweighting large residuals for improved estimation accuracy and quality assessment metrics like NUSE and RLE.
Explores regression analysis, kinetics modeling, parameter estimation, and atmospheric ozone concentration modeling using linear relationships and scatterplots.
Explores graphical model learning with M-estimators, Gaussian process regression, Google PageRank modeling, density estimation, and generalized linear models.
Explores gradient descent methods for smooth convex and non-convex problems, covering iterative strategies, convergence rates, and challenges in optimization.
