Web: http://arxiv.org/abs/2104.13026

Jan. 31, 2022, 2:11 a.m. | Johan Larsson, Jonas Wallin

cs.LG updates on arXiv.org arxiv.org

Predictor screening rules, which discard predictors from the design matrix
before fitting a model, have had considerable impact on the speed with which
l1-regularized regression problems, such as the lasso, can be solved. Current
state-of-the-art screening rules, however, have difficulties in dealing with
highly-correlated predictors, often becoming too conservative. In this paper,
we present a new screening rule to deal with this issue: the Hessian Screening
Rule. The rule uses second-order information from the model to provide more
accurate screening …

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