6 papers
LiDDA: Data Driven Attribution at LinkedIn
John Bencina, Erkut Aykutlug, Yue Chen +4
Data Driven Attribution, which assigns conversion credits to marketing interactions based on causal patterns learned from data, is the foundation of modern marketing intelligence a…
BanditLP: Large-Scale Stochastic Optimization for Personalized Recommendations
Phuc Nguyen, Benjamin Zelditch, Joyce Chen +2
We present BanditLP, a scalable multi-stakeholder contextual bandit framework that unifies neural Thompson Sampling for learning objective-specific outcomes with a large-scale line…
A U-Statistic-based random forest approach for genetic interaction study
Ming Li, Ruo-Sin Peng, Changshuai Wei +1
Variations in complex traits are influenced by multiple genetic variants, environmental risk factors, and their interactions. Though substantial progress has been made in identifyi…
Collapsing ROC approach for risk prediction research on both common and rare variants
Changshuai Wei, Qing Lu
Risk prediction that capitalizes on emerging genetic findings holds great promise for improving public health and clinical care. However, recent risk prediction research has shown…
Causal Predictive Optimization and Generation for Business AI
Liyang Zhao, Olurotimi Seton, Himadeep Reddy Reddivari +4
The sales process involves sales functions converting leads or opportunities to customers and selling more products to existing customers. The optimization of the sales process thu…
A multi-locus predictiveness curve and its summary assessment for genetic risk prediction
Changshuai Wei, Ming Li, Yalu Wen +2
With the advance of high-throughput genotyping and sequencing technologies, it becomes feasible to comprehensive evaluate the role of massive genetic predictors in disease predicti…