Yifan Kang, Kai Liu. Scalable Low-Rank Tensor Completion. Under Review 2026
Kai Liu. Efficient and Scalable Semi-Supervised Metric Learning for High-Dimensional Data. SIAM International Conference on Data Mining, SDM 2026
Yifan Kang, Yarui Cao, Kai Liu. Efficient Block-wise Optimization for Large-Scale Multinomial Logistic Regression. SIAM International Conference on Data Mining, SDM 2026
Yarui Cao, Kai Liu. Exact Sparse Orthogonal Dictionary Learning. The IEEE International Conference on Data Mining, ICDM 2025
Yifan Kang, Kai Liu. Acceleration in Low-Rank Tensor Completion. SIAM International Conference on Data Mining, SDM 2025
Mengyuan Zhang, Kai Liu. On Regularized Sparse Logistic Regression. The IEEE International Conference on Data Mining, ICDM 2023
Mengyuan Zhang, Kai Liu. Multi-Task Learning with Prior Information. SIAM International Conference on Data Mining, SDM 2023
Mengyuan Zhang, Kai Liu. Rethinking Symmetric Matrix Factorization: A More General and Better Clustering Perspective. The IEEE International Conference on Data Mining, ICDM 2022
Mengyuan Zhang, Kai Liu. Enriched Robust Multi-view Kernel Subspace Clustering. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
Xiao Li, Zhihui Zhu, Qiuwei Li, Kai Liu. A Provable Splitting Approach for Symmetric Nonnegative Matrix Factorization. Transactions on Knowledge and Data Engineering, TKDE 2021
Kai Liu, Xiangyu Li, Zhihui Zhu, Lodewijk Brand, Hua Wang. Factor-Bounded Nonnegative Matrix Factorization. ACM Transactions on Knowledge Discovery from Data, TKDD 2021
Zhiyuan Liu, Huazheng Wang, Fan Shen, Kai Liu, Lijun Chen. Incentivized Exploration for Multi-Armed Bandits under Reward Drift. 34th AAAI Conference on Artificial Intelligence, AAAI 2020
Kai Liu, Lou Brand, Hua Wang, Philip Nie. Learning Robust Distance Metric with Side Information via Ratio Minimization of Orthogonally Constrained L21-Norm Distances. International Joint Conference on Artificial Intelligence, IJCAI 2019
Kai Liu, Haoxuan Yang, Hua Wang, Philip Nie. Learning Strictly Orthogonal p-Order Nonnegative Laplacian Embedding via Smoothed Iterative Reweighted Method. International Joint Conference on Artificial Intelligence, IJCAI 2019
Kai Liu, Qiuwei Li, Hua Wang, Gongguo Tang. Spherical Principal Component Analysis. SIAM International Conference on Data Mining, SDM 2019
Lodewijk Brand, Xue Yang, Kai Liu, Saad Elbeleidy, Hua Wang, Hao Zhang. Learning Robust Multi-Label Sample Specific Distances for Identifying HIV-1 Drug Resistance. 23rd Annual International Conference on Research in Computational Molecular Biology, RECOMB 2019
Kai Liu, Hua Wang, Fei Han, Hao Zhang. Visual Place Recognition via Robust L2-Norm Distance Based Holism and Landmark Integration. 33rd AAAI Conference on Artificial Intelligence, AAAI 2019
Zhihui Zhu, Xiao Li, Kai Liu, Qiuwei Li. Dropping Symmetry for Fast Symmetric Nonnegative Matrix Factorization. 32nd annual conference on Neural Information Processing Systems, NIPS 2018
Kai Liu, Hua Wang. High-Order Co-Clustering via Strictly Orthogonal and Symmetric L1-norm Nonnegative Matrix Tri-Factorization. 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
Kai Liu, Hua Wang, Feiping Nie, Hao Zhang. Learning Multi-Instance Enriched Image Representations via Non-Greedy Ratio Maximization of the L1-norm Distances. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018
The Journal of Machine Learning Research (JMLR)
International Conference on Machine Learning (ICML)
International Conference on Learning Representations (ICLR)
Annual Conference on Neural Information Processing Systems (NeurIPS)
International Conference on Artificial Intelligence and Statistics (AISTATS)
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
Convex Optimization (Boyd, Stephen P., and Lieven Vandenberghe)
Introduction to Nonlinear Optimization (Beck, Amir)
First Order Methods in Optimization (Beck, Amir)
Numerical Optimization (Nocedal, Jorge, and Stephen J. Wright)
Matrix Analysis and Applications (Xianda Zhang) [Chinese: 矩阵分析与应用 (张贤达)]
Matrix Analysis (Horn, Roger A., and Charles R. Johnson)
High Dimensional Probability (Vershynin, Roman)
High Dimensional Statistics (Wainwright, Martin)
Statistical Learning with Sparsity (Trevor Hastie, Robert Tibshirani, and Martin Wainwright)
The Elements of Statistical Learning (Hastie T, Tibshirani R, Friedman JH)
Foundations of Machine Learning (Mohri, M., Rostamizadeh, A. and Talwalkar, A.)
An introduction to Statistical Learning (Witten D, James G, Hastie T, Tibshirani R)
Kai has a keen interest in traditional Chinese culture including calligraphy, painting, history, and classical poetry. In his leisure time, he enjoys hiking, jogging, and playing Chinese chess. Above all, he is an avid reader, with a particular fondness for biographies and historical works, such as: Lust for Life, The Gay Genius, The Transformation of China, 胡适晚年身影, and 余英时回忆录.
Kai loves badminton and was a member of the Department of Automation badminton team at Tsinghua University, where he helped the team win the university-level championship. He later won the Tsinghua alumni badminton tournament championship twice, as well as the men’s doubles championship in the Greater Denver area. He completed a half marathon in 2017 and a full marathon in 2018. In addition, he has been a Madridista for over 20 years.
Starting from June 2026, he decided to write more in public, inspired by and in honor of Prof. Bertsekas, and here is the link.
Email: liukaizhijia@gmail.com, kail@clemson.edu
Office: 227 McAdams Hall | Clemson, SC 29634-0901
