Trevor Hastie develops methods in statistical learning, including generalized additive models and regularized regression. His research page describes the interaction between interpretable statistical models, flexible prediction and efficient computation.[ref]
His joint Graphical Lasso work with Jerome H. Friedman and Robert Tibshirani turns sparse precision matrix estimation into repeated Lasso subproblems. A blockwise algorithm updates parts of the estimate while solving the penalized likelihood problem, making large conditional-dependence models computationally feasible.[ref]
Sparse inverse covariance estimation with the graphical lasso by Jerome H. Friedman and others. Algorithm development and coordinate-descent formulation
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Trevor Hastie is a prominent statistician and professor known for his work in the fields of statistics and machine learning. He is a professor at Stanford University and has made significant contributions to various areas, including statistical learning, data mining, and bioinformatics. Hastie is particularly well-known for co-authoring the influential book "The Elements of Statistical Learning," along with Robert Tibshirani and Jerome Friedman.