3 citations · 3 across the 10 of their papers we have counts for
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stat.ME2025
Conformal Inference For Missing Data under Multiple Robust Learning
Wenlu Tang, Hongni Wang, Xingcai Zhou +2
We develop a novel approach to tackle the common but challenging problem of conformal inference for missing data in machine learning, focusing on Missing at Random (MAR) data. We p…
stat.ME2025
Non-Asymptotic Analysis of Online Local Private Learning with SGD
Enze Shi, Jinhan Xie, Bei Jiang +2
Differentially Private Stochastic Gradient Descent (DP-SGD) has been widely used for solving optimization problems with privacy guarantees in machine learning and statistics. Despi…
stat.ME2025
Online differentially private inference in stochastic gradient descent
Jinhan Xie, Enze Shi, Bei Jiang +2
We propose a general privacy-preserving optimization-based framework for real-time environments without requiring trusted data curators. In particular, we introduce a noisy stochas…