Web: http://arxiv.org/abs/1912.12213

June 23, 2022, 1:12 a.m. | Jelena Bradic, Victor Chernozhukov, Whitney K. Newey, Yinchu Zhu

stat.ML updates on arXiv.org arxiv.org

This paper is about the feasibility and means of root-n consistently
estimating linear, mean-square continuous functionals of a high dimensional,
approximately sparse regression. Such objects include a wide variety of
interesting parameters such as regression coefficients, average derivatives,
and the average treatment effect. We give lower bounds on the convergence rate
of estimators of a regression slope and an average derivative and find that
these bounds are substantially larger than in a low dimensional, semiparametric
setting. We also give debiased …

arxiv learning math minimax sparsity

More from arxiv.org / stat.ML updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY