David Bruns-Smith,Oliver Dukes,Avi Feller et al.
David Bruns-Smith et al.
We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning. These popular doubly robust estimators combine outcome modelling with balancing weights-weights that achieve covariate ba...
Minimax and adaptive transfer learning for nonparametric classification under distributed differential privacy constraints [0.03%]
分布式差分隐私约束下非参数分类的MinMax自适应迁移学习方法研究
Arnab Auddy,T Tony Cai,Abhinav Chakraborty
Arnab Auddy
This paper considers minimax and adaptive transfer learning for nonparametric classification under the posterior drift model with distributed differential privacy constraints. Our study is conducted within a heterogeneous framework, encompa...
Multi-task learning for sparsity pattern heterogeneity: statistical and computational perspectives [0.03%]
多任务学习的稀疏模式异质性：统计和计算观点
Kayhan Behdin,Gabriel Loewinger,Kenneth T Kishida et al.
Kayhan Behdin et al.
We consider a problem in multi-task learning (MTL) where multiple linear models are jointly trained on a collection of datasets ('tasks'). A key novelty of our framework is that it allows the sparsity pattern of regression coefficients and ...
Jie Hu,Jiayi Tong,Yang Ning et al.
Jie Hu et al.
Selecting a set of universally relevant features associated with a given response variable across multiple distributed data sites is an important problem in numerous scientific fields. However, performing this federated feature selection ta...
Jinzhou Li,Benjamin B Chu,Ines F Scheller et al.
Jinzhou Li et al.
This work is motivated by the following problem: Can we identify the disease-causing gene in a patient affected by a monogenic disorder? This problem is an instance of root cause discovery. Specifically, we aim to identify the intervened va...
Simplifying debiased inference via automatic differentiation and probabilistic programming [0.03%]
自动微分和概率编程的无偏推理简化方法
Alex Luedtke
Alex Luedtke
We introduce an algorithm that simplifies the construction of efficient estimators, making them accessible to a broader audience. 'Dimple' takes as input computer code representing a parameter of interest and outputs an efficient estimator....
Principal stratification with U-statistics under principal ignorability [0.03%]
主要分层下的U统计量在主要不可识别性条件下的应用
Xinyuan Chen,Fan Li
Xinyuan Chen
Principal stratification is a popular framework for causal inference in the presence of an intermediate outcome. While the principal average treatment effects are the standard target of inference, they may be insufficient when interest lies...
Causal K-Means Clustering [0.03%]
因果K均值聚类
Kwangho Kim,Jisu Kim,Edward H Kennedy
Kwangho Kim
Causal effects are often characterized with population summaries. These might provide an incomplete picture when there are heterogeneous treatment effects across subgroups. Since the subgroup structure is typically unknown, it is more chall...
Zhiwei Xu,Ziming Gan,Doudou Zhou et al.
Zhiwei Xu et al.
The effective analysis of high-dimensional Electronic Health Record (EHR) data, with substantial potential for healthcare research, presents notable methodological challenges. Employing predictive modeling guided by a knowledge graph (KG), ...
Correction to: Inference of dependency knowledge graph for Electronic Health Records [0.03%]
对电子健康记录依赖知识图谱推断的改正
[This corrects the article DOI: 10.1093/jrsssb/qkaf061.]. © The Royal Statistical Society 2025. All rights reserved. For commercial re-use, please conta...