Yanhao Jin — accepted AI-conference papers

2026

Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regression

Haodong Liang, Yanhao Jin, Krishna Balasubramanian, Lifeng Lai

ICLR 2026poster

We study instrumental variable regression (IVaR) under differential privacy constraints. Classical IVaR methods (like two-stage least squares regression) rely on solving moment equations that directly use sensitive covariates and instruments, creating significant risks of privacy leakage and posing…

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2026

Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation

Yanhao Jin, Krishna Balasubramanian, Debashis Paul

ICML 2026poster

Meta-learning involves training models on a variety of training tasks in a way that enables them to generalize well on new, unseen test tasks. In this work, we consider meta-learning within the framework of high-dimensional multivariate random-effects linear models and study generalized ridge-regres…

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