Faults tolerant design on ReRAM (Update)

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Reference Paper

• Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. Preprint Arxiv.1603.02754

Technical Highlights

• Sparse aware tree learning to optimize for sparse data.
• Distributed weighted quantile sketch for quantile findings and approximate tree learning.
• Cache aware learning algorithm
• Out of core computation system for training when

Impact

• XGBoost is one of the most frequently used package to win machine learning challenges.
• XGBoost can solve billion scale problems with few resources and is widely adopted in industry.
• See XGBoost Resources Page for a complete list of usecases of XGBoost, including machine learning challenge winning solutions, data science tutorials and industry adoptions.

Acknowledgement

XGBoost open source project is actively developed by amazing contributors from DMLC/XGBoost community.

Resources

• Tutorial on Tree Boosting [Slides]
• XGBoost Main Project Repo for python, R, java, scala and distributed version.
• XGBoost Julia Package
• XGBoost Resources for all resources including challenge winning solutions, tutorials. –>