A Support Detection and Root Finding Approach for Learning High-dimensional Generalized Linear Models. - Jian Huang 003; Yuling Jiao; Lican Kang; Jin Liu 011; Yanyan Liu; Xiliang Lu (CoRR, 2020)

Convergence Analysis of Schr{ö}dinger-F{ö}llmer Sampler without Convexity. - Yuling Jiao; Lican Kang; Yanyan Liu; Youzhou Zhou (CoRR, 2021)

A data-driven line search rule for support recovery in high-dimensional data analysis. - Peili Li; Yuling Jiao; Xiliang Lu; Lican Kang (Comput. Stat. Data Anal., 2022)

GSDAR: a fast Newton algorithm for ℓ 0 regularized generalized linear models with statistical guarantee. - Jian Huang 003; Yuling Jiao; Lican Kang; Jin Liu 011; Yanyan Liu; Xiliang Lu (Comput. Stat., 2022)

Error Analysis of Fitted Q-iteration with ReLU-activated Deep Neural Networks. - Lican Kang; Han Yuan; Chang Zhu (Tiny Papers @ ICLR, 2023)

An Empirical Study of the Effect of Background Data Size on the Stability of SHapley Additive exPlanations (SHAP) for Deep Learning Models. - Han Yuan; Mingxuan Liu; Lican Kang; Chenkui Miao; Ying Wu (Tiny Papers @ ICLR, 2023)

Fast Excess Risk Rates via Offset Rademacher Complexity. - Chenguang Duan; Yuling Jiao; Lican Kang; Xiliang Lu; Jerry Zhijian Yang (ICML, 2023)

Over-parameterized Deep Nonparametric Regression for Dependent Data with Its Applications to Reinforcement Learning. - Xingdong Feng; Yuling Jiao; Lican Kang; Baqun Zhang; Fan Zhou (J. Mach. Learn. Res., 2023)

Global Optimization via Schrödinger-Föllmer Diffusion. - Yin Dai; Yuling Jiao; Lican Kang; Xiliang Lu; Jerry Zhijian Yang (SIAM J. Control. Optim., 2023)

Deep estimation for Q⁎ with minimax Bellman error minimization. - Lican Kang; Xu Liao; Jin Liu 011; Yuan Luo (Inf. Sci., 2023)

Latent Schr{ "o}dinger Bridge Diffusion Model for Generative Learning. - Yuling Jiao; Lican Kang; Huazhen Lin; Jin Liu; Heng Zuo (CoRR, 2024)

Model Free Prediction with Uncertainty Assessment. - Yuling Jiao; Lican Kang; Jin Liu; Heng Peng; Heng Zuo (CoRR, 2024)

A communication-efficient method for generalized linear regression with ℓ 0 regularization. - Kunpeng Wang; Xuerui Li; Yanyan Liu; Lican Kang (Commun. Stat. Simul. Comput., 2024)

Newton-Raphson Meets Sparsity: Sparse Learning Via a Novel Penalty and a Fast Solver. - Yongxiu Cao; Lican Kang; Xuerui Li; Yanyan Liu; Yuan Luo; Yueyong Shi (IEEE Trans. Neural Networks Learn. Syst., 2024)

Deep contrastive representation learning for supervised tasks. - Chenguang Duan; Yuling Jiao; Lican Kang; Jerry Zhijian Yang; Fusheng Zhou (Pattern Recognit., 2025)

PDAS: a Newton-type method for L0 regularized accelerated failure time model. - Ning Su; Yanyan Liu; Lican Kang (Comput. Stat., 2025)

Schrödinger-Föllmer Sampler. - Jian Huang 003; Yuling Jiao; Lican Kang; Xu Liao; Jin Liu 011; Yanyan Liu (IEEE Trans. Inf. Theory, 2025)

Approximate Policy Iteration With Deep Minimax Average Bellman Error Minimization. - Lican Kang; Yuhui Liu; Yuan Luo; Jerry Zhijian Yang; Han Yuan; Chang Zhu (IEEE Trans. Neural Networks Learn. Syst., 2025)

Opening the black box of deep learning: Validating the statistical association between explainable artificial intelligence (XAI) and clinical domain knowledge in fundus image-based glaucoma diagnosis. - Han Yuan; Lican Kang; Yong Li (CoRR, 2025)

Model Free Prediction With Uncertainty Assessment. - Yuling Jiao; Lican Kang; Jin Liu 011; Heng Peng; Heng Zuo (IEEE Trans. Inf. Theory, 2025)