Parallel boosted regression trees for web search ranking

By Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal, Jennifer Paykin. 2011

190 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

XGBoost: A Scalable Tree Boosting System (2016, methodology): “Notably, it is also possible to directly construct approximate histograms of gradient statistics [22].”
LightGBM: A Highly Efficient Gradient Boosting Decision Tree (2017, methodology): “There have been quite a few implementations of GBDT in the literature, including XGBoost [13], pGBRT [14], scikit-learn [15], and gbm in R [16] 4 .”
XGBoost : Reliable Large-scale Tree Boosting System (2015, background): “While there is some discussion on how to parallelize this type of algorithm [9, 3, 11], there is little discussion about optimizing system and algorithm jointly in order to build a reliable tree boosting system that handles billion scale problems.”
A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment (2017, background): “Finally, Section 6 presents a conclusion and future work.”
Gradient boosted feature selection (2014, background): “C++) with the incorporation of known parallelization techniques for limited depth trees [28].”
Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting (2016, background): “A distributed version can be found at [Tyree et al., 2011].”
Efficient Cost-Aware Cascade Ranking in Multi-Stage Retrieval (2017, methodology): “e rst set of features are a large collection of pre-retrieval features commonly used for predicting query diculty [8], and more recently within LTR [21, 34] were gathered.”
Intelligible models for classification and regression (2012, methodology): “[20] parallelizes growing regression trees via gradient boosting using a master-worker paradigm where data are partitioned among workers.”
VHT: Vertical hoeffding tree (2016, background): “For this reason, they are commonly used in one of the most time-sensitive domains nowadays – Web search [6, 17].”
General Heterogeneous Transfer Distance Metric Learning via Knowledge Fragments Transfer (2017, background): “The tree is greedily learned by pGBRT [Tyree et al., 2011].”
Training Efficient Tree-Based Models for Document Ranking (2013, background): “The effectiveness of tree-based ensembles for learning to rank has been widely demonstrated: an example is the family of gradient-boosted regression trees (GBRTs) [9, 3, 10, 2].”