本次讲座将介绍一种新的 Boosting 方法：Boosted 决策表。我们会介绍学习决策表的方法并讨论为什么决策表比标准的决策树更适合 Gradient Boosting 框架。我们还会介绍一种高效的数据结构来存储及表示决策表，从而减少预测阶段的时间开销。最后我们会用 LinkedIn news feed 作为实际案例讨论决策表在实践中的应用。

In this talk I will present gradient boosted decision tables (BDTs). I will present novel algorithms to fit decision tables and discuss why decision tables are better weak learner in the gradient boosting framework. In addition, I will talk about efficient data structures to represent decision tables and a novel fast algorithm to improve the scoring efficiency for boosted ensemble of decision tables. In the end, I will also discuss our successful deployment boosted decision tables to LinkedIn news feed system that achieved significant lift on key metrics. This work has been published in KDD'17.