Large-Scale Machine Learning at UW

Machine learning and data-driven approaches are becoming very important in many areas. It is increasing important to make scalable machine learning solutions for learning This is the project page about this joint effort at University of Washington.

Most of the projects listed in this page is also part of Distributed Machine Learning Common initiative. Which is a collaborative effort among machine learning research groups in several institutions to making cutting-edge large-scale machine learning research widely available.

Projects

XGBoost: A Scalable Tree Boosting System

XGBoost is an optimized distributed gradient boosting system with It is widely used by winners of machine learning challenges and machine learning engineers in industry.

MXNet: Efficient and Flexible Deep Learning

MXNet is a deep learning library that combines the flexiblity and power of declartive programing. This is a project created in collaboration with researchers from UW, CMU, NYU, NUS, MIT and developed together with many others.

Rabit: Reliable Allreduce and Broadcast Interface

A light weight library that provides a fault tolerant interface of Allreduce and Broadcast for portable , scalable and reliable distributed machine learning programs.