Cheng Chen
Shanghai Jiao Tong University
14 Papers
88 Citations
Cheng Chen is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Computer science & Optimization problem. The author has an hindex of 4, co-authored 13 publications.
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Papers
Combining Factorization Model and Additive Forest for Collaborative Followee Recommendation
Tianqi Chen,Linpeng Tang,Qin Liu,Diyi Yang,Saining Xie,Xuezhi Cao,Chunyang Wu,Enpeng Yao,Zhengyang Liu,Zhansheng Jiang,Cheng Chen,Weihao Kong,Yong Yu +12 more
- 01 Jan 2012
TL;DR: The modeling approach is able to utilize various side information provided by the challenge dataset, and thus alleviates the cold-start problem, and the new temporal dynamics model the authors have proposed using an additive forest can automatically adjust the splitting time points to model popularity evolution more accurately.
Fast Fisher discriminant analysis with randomized algorithms
TL;DR: Fast FDA algorithms based on random projection and random feature map are proposed to accelerate FDA and kernel FDA and give theoretical guarantee that the fast FDA algorithms using random projection have good generalization ability in comparison with the conventional regularized FDA.
36
•Posted Content
Robust Frequent Directions with Application in Online Learning
TL;DR: A new sketching strategy called robust frequent directions (RFD) is proposed by introducing a regularization term that reduces the approximation error of FD without increasing the computational cost and derives a regret bound for the online Newton algorithm based on RFD.
17
•Posted Content
A Stochastic Proximal Point Algorithm for Saddle-Point Problems.
TL;DR: A stochastic proximal point algorithm, which accelerates the variance reduction method SAGA for saddle point problems and adopts the algorithm to policy evaluation and the empirical results show that the method is much more efficient than state-of-the-art methods.
16
•Journal Article
Robust Frequent Directions with Application in Online Learning
TL;DR: In this article, a robust frequent directions (RFD) strategy is proposed to overcome the rank deficiency problem by introducing a regularization term, which reduces the approximation error of FD without increasing the computational cost.