Taifeng Wang
Alibaba Group
79 Papers
559 Citations
Taifeng Wang is an academic researcher from Alibaba Group. The author has contributed to research in topics: Computer science & Graph (abstract data type). The author has an hindex of 18, co-authored 72 publications. Previous affiliations of Taifeng Wang include University of Science and Technology of China & Microsoft.
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Papers
•Proceedings Article
LightGBM: a highly efficient gradient boosting decision tree
Guolin Ke,Qi Meng,Thomas Finley,Taifeng Wang,Wei Chen,Weidong Ma,Qiwei Ye,Tie-Yan Liu +7 more
- 04 Dec 2017
TL;DR: It is proved that, since the data instances with larger gradients play a more important role in the computation of information gain, GOSS can obtain quite accurate estimation of the information gain with a much smaller data size, and is called LightGBM.
•Posted Content
Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks
TL;DR: Wang et al. as discussed by the authors introduced a novel framework based on Recurrent Neural Networks (RNN), which directly models the dependency on user's sequential behaviors into the click prediction process through the recurrent structure in RNN.
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•Proceedings Article
Asynchronous Stochastic Gradient Descent with delay compensation
Shuxin Zheng,Qi Meng,Taifeng Wang,Wei Chen,Nenghai Yu,Zhi-Ming Ma,Tie-Yan Liu +6 more
- 06 Aug 2017
TL;DR: The proposed algorithm is evaluated on CIFAR-10 and ImageNet datasets, and the experimental results demonstrate that DC-ASGD outperforms both synchronous SGD and asynchronous SGD, and nearly approaches the performance of sequential SGD.
•Proceedings Article
Sequential click prediction for sponsored search with recurrent neural networks
Yuyu Zhang,Hanjun Dai,Chang Xu,Jun Feng,Taifeng Wang,Jiang Bian,Bin Wang,Tie-Yan Liu +7 more
- 27 Jul 2014
TL;DR: A novel framework based on Recurrent Neural Networks (RNN) is introduced that directly models the dependency on user's sequential behaviors into the click prediction process through the recurrent structure in RNN.
•Proceedings Article
A Highly Efficient Gradient Boosting Decision Tree
Guolin Ke,Qi Meng,Taifeng Wang,Wei Chen,Weidong Ma,Tie-Yan Liu +5 more
- 01 Jan 2017
TL;DR: It is proved that, since the data instances with larger gradients play a more important role in the computation of information gain, GOSS can obtain quite accurate estimation of the information gain with a much smaller data size.