13 citations · 36 across the 13 of their papers we have counts for
8 papers · 1 filter
DeCom: Deep Coupled-Factorization Machine for Post COVID-19 Respiratory Syncytial Virus Prediction with Nonpharmaceutical Interventions Awareness
Xinyan Li, Cheng Qian, Lucas Glass
Respiratory syncytial virus (RSV) is one of the most dangerous respiratory diseases for infants and young children. Due to the nonpharmaceutical intervention (NPI) imposed in the C…
GOCPT: Generalized Online Canonical Polyadic Tensor Factorization and Completion
Chaoqi Yang, Cheng Qian, Jimeng Sun
Low-rank tensor factorization or completion is well-studied and applied in various online settings, such as online tensor factorization (where the temporal mode grows) and online t…
Clinical trial site matching with improved diversity using fair policy learning
Rakshith S Srinivasa, Cheng Qian, Brandon Theodorou +4
The ongoing pandemic has highlighted the importance of reliable and efficient clinical trials in healthcare. Trial sites, where the trials are conducted, are chosen mainly based on…
JULIA: Joint Multi-linear and Nonlinear Identification for Tensor Completion
Cheng Qian, Kejun Huang, Lucas Glass +2
Tensor completion aims at imputing missing entries from a partially observed tensor. Existing tensor completion methods often assume either multi-linear or nonlinear relationships…
Multi-version Tensor Completion for Time-delayed Spatio-temporal Data
Cheng Qian, Nikos Kargas, Cao Xiao +3
Real-world spatio-temporal data is often incomplete or inaccurate due to various data loading delays. For example, a location-disease-time tensor of case counts can have multiple d…
STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization
Nikos Kargas, Cheng Qian, Nicholas D. Sidiropoulos +3
Accurate prediction of the transmission of epidemic diseases such as COVID-19 is crucial for implementing effective mitigation measures. In this work, we develop a tensor method to…
SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors
Ardavan Afshar, Kejing Yin, Sherry Yan +4
Existing tensor factorization methods assume that the input tensor follows some specific distribution (i.e. Poisson, Bernoulli, and Gaussian), and solve the factorization by minimi…
REP: Predicting the Time-Course of Drug Sensitivity
Cheng Qian, Amin Emad, Nicholas D. Sidiropoulos
The biological processes involved in a drug's mechanisms of action are oftentimes dynamic, complex and difficult to discern. Time-course gene expression data is a rich source of in…