1 citations · 2 across the 14 of their papers we have counts for
15 papers
Locally Private Inference for Riemannian Stochastic Optimization
Xiaotian Chang, Yangdi Jiang, Qirui Hu
We develop inference for manifold-valued population minimizers when each observation belongs to a different participant and only locally private messages reach the analyst. The met…
Riemannian Simultaneous Inference for Tangent Vector Field Regression
Xiaotian Chang, Yangdi Jiang, Qirui Hu
We consider nonparametric tangent vector field regression on a Riemannian manifold without boundary. Because responses at different points lie in different tangent spaces, the prop…
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…
Approximation Theorems for High-Dimensional Canonical U-Statistics: Gaussian Chaos and Phase Transition
Leheng Cai, Qirui Hu
We study simultaneous inference for maxima of canonical order-two -statistics in high dimension. Degeneracy makes quadratic fluctuations leading, so ordinary Gaussian calibratio…
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…
Locally Private Online Quantile Regression: Estimation and Inference
Yi Liu, Qirui Hu
We study estimation and inference for online quantile regression under a one-report user-level $\eps$-locally differentially private ($\eps$-LDP) protocol. The main difficulty is t…
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…
Asymptotic Anytime-Valid Inference for U-statistics
Leheng Cai, Qirui Hu, Weijia Li
We study asymptotic anytime-valid confidence sequences for degree-two U-statistics under continuous monitoring. In the nondegenerate case, Hoeffding's projection reduces the proble…
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 (…
Unified theory of testing relevant hypotheses in functional time series
Leheng Cai, Qirui Hu
In this paper, we develop a {\em unified} framework for testing relevant hypotheses in functional time series. The proposed approach accommodates one-sample, two-sample, and change…
From sparse to dense functional time series: phase transitions of detecting structural breaks and beyond
Leheng Cai, Qirui Hu
We develop a novel methodology for detecting abrupt break points in mean functions of functional time series, adaptable to arbitrary sampling schemes. By employing B-spline smoothi…
From Sparse to Dense Functional Data: Phase Transitions from a Simultaneous Inference Perspective
Leheng Cai, Qirui Hu
We aim to develop simultaneous inference tools for the mean function of functional data from sparse to dense. First, we derive a unified Gaussian approximation to construct simulta…
Online Local Differential Private Quantile Inference via Self-normalization
Yi Liu, Qirui Hu, Lei Ding +2
Based on binary inquiries, we developed an algorithm to estimate population quantiles under Local Differential Privacy (LDP). By self-normalizing, our algorithm provides asymptotic…