all AI news
The Selectively Adaptive Lasso. (arXiv:2205.10697v4 [stat.ML] UPDATED)
Sept. 28, 2022, 1:13 a.m. | Alejandro Schuler, Yi Li, Mark van der Laan
stat.ML updates on arXiv.org arxiv.org
Machine learning regression methods allow estimation of functions without
unrealistic parametric assumptions. Although they can perform exceptionally in
prediction error, most lack theoretical convergence rates necessary for
semi-parametric efficient estimation (e.g. TMLE, AIPW) of parameters like
average treatment effects. The Highly Adaptive Lasso (HAL) is the only
regression method proven to converge quickly enough for a meaningfully large
class of functions, independent of the dimensionality of the predictors.
Unfortunately, HAL is not computationally scalable. In this paper we build upon …
More from arxiv.org / stat.ML updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Senior Data Analyst - SQL
@ Experian | Heredia, Costa Rica
Lead Business Intelligence Developer
@ L.A. Care Health Plan | Los Angeles, CA, US, 90017
(USA) Senior Manager, Data Analytics
@ Walmart | (USA) AR BENTONVILLE Home Office J Street Offices, Suite #2
Autonomous Haulage System Application Specialist
@ Komatsu | Belo Horizonte, BR
Machine Learning Engineer
@ GFT Technologies | Alcobendas, M, ES, 28108