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 graphical model learning with M-estimators, Gaussian process regression, Google PageRank modeling, density estimation, and generalized linear models.
Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.
