Computationally and efficient inference for complex large-scale data. Abstracts from the workshop held March 6--12, 2016
type: Item
instance of: scholarly article
title: Computationally and efficient inference for complex large-scale data. Abstracts from the workshop held March 6--12, 2016
zbMATH Open document ID: 1380.00035
DOI: 10.4171/OWR/2016/16
published in: Oberwolfach Reports
publication date: 2018-02-21T00:00:00Z
review text: Summary: The aim of the highly successful workshop \textit{Computationally and statistically efficient inference for large-scale and heterogeneous data} was to foster dissemination and collaboration between researchers in the area of highdimensional and large-scale data analysis. The field has grown tremendously over the last decade. Faced with ever larger data sets, many algorithms have emerged in computer science, machine learning and statistics that allow computationally efficient manipulation and model fitting on large datasets. Yet the mathematical and statistical properties of these algorithms are only just beginning to be understood. Advancing the field is important to avoid many misleading scientific discoveries based on pure data manipulation without the accompanying mathematical insights. The talks and discussions at the workshop covered the latest advances from optimization to statistical error control for large-scale data analysis.
Mathematics Subject Classification ID: 00B05
Mathematics Subject Classification ID: 00B25
Mathematics Subject Classification ID: 62Gxx
Mathematics Subject Classification ID: 62Hxx
Mathematics Subject Classification ID: 62Jxx
Mathematics Subject Classification ID: 62C20
Mathematics Subject Classification ID: 62-06
Mathematics Subject Classification ID: 65C60
zbMATH DE Number: 6841681
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.4171/owr/2016/16
OpenAlex ID: W2530667785
description: scientific article; zbMATH DE number 6841681