Machine learning addresses the question of how to build computers that improve
automatically through experience. It is one of today's most rapidly growing technical fields …

The random forest algorithm, proposed by L. Breiman in 2001, has been extremely
successful as a general-purpose classification and regression method. The approach, which …

We present a communication-efficient surrogate likelihood (CSL) framework for solving
distributed statistical inference problems. CSL provides a communication-efficient surrogate …

The recent advent of remote sensing, mobile technologies, novel transaction systems, and
highperformance computing offers opportunities to understand trends, behaviors, and …

We study the use of randomized value functions to guide deep exploration in reinforcement
learning. This offers an elegant means for synthesizing statistically and computationally …

Editor's note: This editorial launches a series written by editors and co-authored with a
senior executive, thought leader, or scholar from a different field to explore new content …

Consistency of random forests Page 1 The Annals of Statistics 2015, Vol. 43, No. 4, 1716–1741
DOI: 10.1214/15-AOS1321 © Institute of Mathematical Statistics, 2015 CONSISTENCY OF …

Big Data is one of the major challenges of statistical science and has numerous
consequences from algorithmic and theoretical viewpoints. Big Data always involve massive …

A useful definition of 'big data'is data that is too big to process comfortably on a single
machine, either because of processor, memory, or disk bottlenecks. Graphics processing …

This paper studies hypothesis testing and parameter estimation in the context of the divide-
and-conquer algorithm. In a unified likelihood based framework, we propose new test …