DMLC is a community of awesome distributed machine learning projects, including the well-known parallel gradient boost tree model
XGBoost, and the deep learning system,
MXNet, etc.
XGBoost is an optimized distributed gradient boosting library designed to be
highly efficient, flexible and portable. It implements machine learning
algorithms under the Gradient Boosting framework. It has been
the
winner solution of many Kaggle machine learning competitions.
Nan Zhu, a member of CPSLAB, is leading the
efforts on the development of
XGBoost jvm-packages and serves as the committee member of DMLC. The main goal of XGBoost jvm-packages is to achieve seamless integration between XGBoost and JVM-based parallel data processing systems like Apache Spark. With integration, users can enjoy both the convenient interfaces in systems like Spark and the high performance of XGBoost.
You can check the
release blog.
A Study of Facebook Likes
SafeVChat: Safety and Security in Online Video Chat Systems
Selected publications:
ISC: Adult Account Detection on Twitter
Selected publications: