Lican Kang

According to our database, Lican Kang authored at least 15 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2025

Deep contrastive representation learning for supervised tasks.

Chenguang Duan, Yuling Jiao, Lican Kang, Jerry Zhijian Yang, Fusheng Zhou

Pattern Recognit., 2025

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., September, 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., August, 2024

Model Free Prediction with Uncertainty Assessment.

Yuling Jiao, Lican Kang, Jin Liu, Heng Peng, Heng Zuo

CoRR, 2024

Latent Schr{\"o}dinger Bridge Diffusion Model for Generative Learning.

Yuling Jiao, Lican Kang, Huazhen Lin, Jin Liu, Heng Zuo

CoRR, 2024

2023

Deep estimation for <i>Q</i>^⁎ with minimax Bellman error minimization.

Lican Kang, Xu Liao, Jin Liu, Yuan Luo

Inf. Sci., November, 2023

Global Optimization via Schrödinger-Föllmer Diffusion.

Yin Dai, Yuling Jiao, Lican Kang, Xiliang Lu, Jerry Zhijian Yang

SIAM J. Control. Optim., October, 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

Fast Excess Risk Rates via Offset Rademacher Complexity.

Chenguang Duan, Yuling Jiao, Lican Kang, Xiliang Lu, Jerry Zhijian Yang

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

Error Analysis of Fitted Q-iteration with ReLU-activated Deep Neural Networks.

Lican Kang, Han Yuan, Chang Zhu

Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

2022

GSDAR: a fast Newton algorithm for ℓ ₀ regularized generalized linear models with statistical guarantee.

Jian Huang, Yuling Jiao, Lican Kang, Jin Liu, Yanyan Liu, Xiliang Lu

Comput. Stat., 2022

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

2021

Convergence Analysis of Schr{ö}dinger-F{ö}llmer Sampler without Convexity.

Yuling Jiao, Lican Kang, Yanyan Liu, Youzhou Zhou

CoRR, 2021

2020

A Support Detection and Root Finding Approach for Learning High-dimensional Generalized Linear Models.

Jian Huang, Yuling Jiao, Lican Kang, Jin Liu, Yanyan Liu, Xiliang Lu

CoRR, 2020