1 citations · 2 across the 14 of their papers we have counts for
10 papers · 1 filter
Asymptotic Anytime-Valid Quantile Inference under Local Differential Privacy
Leheng Cai, Qirui Hu, Shuyuan Wu
Sequential quantile inference is difficult under local differential privacy because every record is randomized before reaching the analyst and the limiting quantile variance depend…
Multicollinearity-agnostic feature screening for non-Euclidean responses: a factor adjusted approach
Moshu Xu, Leheng Cai, Yanmei Shi +3
In high-dimensional settings, multicollinearity is a pervasive issue that can substantially impair the performance of feature screening methods based on marginal Fréchet regression…
MATCH: Multiplier-Assisted Tests for Conditional Hypotheses in Non-Euclidean Data
Leheng Cai, Xu Guo, Qirui Hu
We propose a new procedure MATCH (Multiplier-Assisted Tests for Conditional Hypotheses) to test whether the non-Euclidean data match the target model, which is a general framework…
Differentially private inference framework for Riemannian manifold data
Yangdi Jiang, Xiaotian Chang, Qirui Hu
We propose a novel and systematic differentially private (DP) inference framework for non-Euclidean data. First, we design two types of DP mechanisms for the Fréchet mean and varia…
Individualized Causal Effects under Network Interference with Combinatorial Treatments
Yunping Lu, Haoang Chi, Qirui Hu +1
Modern causal decision-making increasingly demands individualized treatment-effect estimation in networks where interventions are high-dimensional, combinatorial vectors. While net…
Federated Learning of Quantile Inference under Local Differential Privacy
Leheng Cai, Qirui Hu, Shuyuan Wu
In this paper, we investigate federated learning for quantile inference under local differential privacy (LDP). We propose an estimator based on local stochastic gradient descent (…