Qilian Yu
University of California, Davis
14 Papers
35 Citations
Qilian Yu is an academic researcher from University of California, Davis. The author has contributed to research in topics: Streaming algorithm & Greedy algorithm. The author has an hindex of 4, co-authored 13 publications. Previous affiliations of Qilian Yu include Electronic Arts & Zhejiang University.
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Papers
On the puncturing patterns for punctured polar codes
Liang Zhang,Zhaoyang Zhang,Xianbin Wang,Qilian Yu,Chen Yan +4 more
- 11 Aug 2014
TL;DR: A search algorithm is proposed to design good punctured polar codes, and it is proved that designing the optimal puncturing pattern for output bits is equivalent to finding the optimal rhythm for frozen bits.
107
Submodular maximization with multi-knapsack constraints and its applications in scientific literature recommendations
Qilian Yu,Easton Li Xu,Shuguang Cui +2 more
- 02 Dec 2016
TL;DR: This paper proposes a streaming algorithm that achieves a (1/1+2D − ε)-approximation of the optimal value, while it only needs one single pass through the dataset without storing all the data in the memory.
24
Streaming Algorithms for News and Scientific Literature Recommendation: Monotone Submodular Maximization With a $d$ -Knapsack Constraint
Qilian Yu,Li Xu,Shuguang Cui +2 more
TL;DR: This paper proposes a streaming algorithm that achieves both execution speedup and memory saving by several orders of magnitude, compared with existing approaches.
21
Fast Budgeted Influence Maximization Over Multi-Action Event Logs
TL;DR: In this paper, a credit distribution-based model, termed as the multi-action CD (mCD) model, is introduced to quantify the influence ability of each user, which works with practical datasets where one type of action could be recorded for multiple times.
18
Feature selection with interactions in logistic regression models using multivariate synergies for a GWAS application
TL;DR: This paper provides rigorous theoretical analysis and empirical evidence on why the information-theoretic multivariate synergy helps with identifying genetic risk factors via synergistic interactions, and establishes the rigorous sample complexity analysis on detecting interactive effects.