67 citations · 111 across the 41 of their papers we have counts for
21 papers · 1 filter
Multi-Task Active Learning with Efficient Resource Allocation
Hanwen Ye, Jiuchen Zhang, Annie Qu
Many scientific studies allow costly auxiliary information to be collected during data labeling but not at deployment. Examples include diagnostic tests, laboratory assays, and exp…
Risk-Aware Quantile Learning for Personalized Dynamic Treatment Regimes
Chunyin Lei, Annie Qu
Sequential clinical decision-making often involves more than maximizing average efficacy. Clinicians may need to simultaneously optimize clinically relevant tails of the outcome di…
Testing Cumulative Treatment Effects in Continuous Time with ODE Dynamics and Noisy Observations
Jiuchen Zhang, Annie Qu
We develop a semiparametric continuous time inferential framework for testing cumulative treatment effects, including both immediate and carryover effects. The latent state process…
Distributional Treatment Effect Transportability across Heterogeneous Sites
Borna Bateni, Yubai Yuan, Qi Xu +1
We study distributional transportability of treatment effects in a ``cross-site, one-armed target" design, where both treated and control units are observed in a source site, but o…
Partially-shared Imaging Regression on Integrating Heterogeneous Brain-Cognition Associations across Alzheimer's Diagnoses
Yang Sui, Qi Xu, Ting Li +2
Alzheimer's Disease Neuroimaging Initiative (ADNI) diagnostic groups present strong heterogeneous associations among demographic, imaging, and cognitive data. We propose a novel PA…
Individualized Dynamic Mediation Analysis Using Latent Factor Models
Yijiao Zhang, Yubai Yuan, Yuexia Zhang +2
Mediation analysis plays a crucial role in causal inference as it can investigate the pathways through which treatment influences outcome. Most existing mediation analysis assumes…
Individualized Dynamic Latent Factor Model for Multi-resolutional Data with Application to Mobile Health
Jiuchen Zhang, Fei Xue, Qi Xu +2
Mobile health has emerged as a major success for tracking individual health status, due to the popularity and power of smartphones and wearable devices. This has also brought great…
Multi-Label Residual Weighted Learning for Individualized Combination Treatment Rule
Qi Xu, Xiaoke Cao, Geping Chen +3
Individualized treatment rules (ITRs) have been widely applied in many fields such as precision medicine and personalized marketing. Beyond the extensive studies on ITR for binary…
Optimal Individualized Treatment Rule for Combination Treatments Under Budget Constraints
Qi Xu, Haoda Fu, Annie Qu
The individualized treatment rule (ITR), which recommends an optimal treatment based on individual characteristics, has drawn considerable interest from many areas such as precisio…
De-confounding causal inference using latent multiple-mediator pathways
Yubai Yuan, Annie Qu
Causal effect estimation from observational data is one of the essential problems in causal inference. However, most estimation methods rely on the strong assumption that all confo…
Crowdsourcing Utilizing Subgroup Structure of Latent Factor Modeling
Qi Xu, Yubai Yuan, Junhui Wang +1
Crowdsourcing has emerged as an alternative solution for collecting large scale labels. However, the majority of recruited workers are not domain experts, so their contributed labe…
Estimating Optimal Infinite Horizon Dynamic Treatment Regimes via pT-Learning
Wenzhuo Zhou, Ruoqing Zhu, Annie Qu
Recent advances in mobile health (mHealth) technology provide an effective way to monitor individuals' health statuses and deliver just-in-time personalized interventions. However,…
Weak signal identification and inference in penalized likelihood models for categorical responses
Yuexia Zhang, Peibei Shi, Zhongyi Zhu +2
Penalized likelihood models are widely used to simultaneously select variables and estimate model parameters. However, the existence of weak signals can lead to inaccurate variable…
Dynamic Tensor Recommender Systems
Yanqing Zhang, Xuan Bi, Niansheng Tang +1
Recommender systems have been extensively used by the entertainment industry, business marketing and the biomedical industry. In addition to its capacity of providing preference-ba…
Multicategory Angle-based Learning for Estimating Optimal Dynamic Treatment Regimes with Censored Data
Fei Xue, Yanqing Zhang, Wenzhuo Zhou +2
An optimal dynamic treatment regime (DTR) consists of a sequence of decision rules in maximizing long-term benefits, which is applicable for chronic diseases such as HIV infection…
Integrating multi-source block-wise missing data in model selection
Fei Xue, Annie Qu
For multi-source data, blocks of variable information from certain sources are likely missing. Existing methods for handling missing data do not take structures of block-wise missi…
Community Detection with Dependent Connectivity
Yubai Yuan, Annie Qu
In network analysis, within-community members are more likely to be connected than between-community members, which is reflected in that the edges within a community are intercorre…
Semi-orthogonal Non-negative Matrix Factorization with an Application in Text Mining
Jack Yutong Li, Ruoqing Zhu, Annie Qu +2
Emergency Department (ED) crowding is a worldwide issue that affects the efficiency of hospital management and the quality of patient care. This occurs when the request for an admi…
Individualized Multi-directional Variable Selection
Xiwei Tang, Fei Xue, Annie Qu
In this paper we propose a heterogeneous modeling framework which achieves individual-wise feature selection and individualized covariates' effects subgrouping simultaneously. In c…
Semi-standard partial covariance variable selection when irrepresentable conditions fail
Fei Xue, Annie Qu
Traditional variable selection methods could fail to be sign consistent when irrepresentable conditions are violated. This is especially critical in high-dimensional settings when…
Weak Signal Identification and Inference in Penalized Model Selection
Peibei Shi, Annie Qu
Weak signal identification and inference are very important in the area of penalized model selection, yet they are under-developed and not well-studied. Existing inference procedur…