I am looking for highly self-motivated PhD. students to work on Machine/Deep Learning, those who have solid background in Mathematics (Statistics/Optimization) and Programming are encouraged to send me your CV and transcripts to: kail@clemson.edu
Kai Liu
Kai Liu is an Assistant Professor in Computer Science Division, Clemson University. He received his Ph.D. in Computer Science from Colorado School of Mines working with Dr. Hua Wang. His main research interest lies in the intersections of Machine Learning and Optimization with Applications in Data Mining. He aims to develop Robust, Efficient and Scalable data-driven machine learning algorithms with theoretical guarantee. Besides, Graph Neural Networks (GNN) and Diffusion Models are his current research areas in Deep Learning.
Yifan Kang, Kai Liu. Nesterov Meets Robust Multitask Learning Twice. NeurIPS OPT 2023
Mengyuan Zhang, Kai Liu. On Regularized Sparse Logistic Regression. The IEEE International Conference on Data Mining, ICDM 2023
Mengyuan Zhang, Kai Liu. Strictly Low Rank Constraint Optimization. ICML SODS 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 Workshops
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
Haoxuan Yang*, Kai Liu*, 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
Kai Liu, Hua Wang. Robust Multi-Relational Clustering via L1-norm Symmetric Non-negative Matrix Factorization. 53rd Annual Meeting of the Association for Computational Linguistics, ACL 2015
CPSC 8810 Optimization for Machine Learning, Clemson University
CPSC 8420 Advanced Machine Learning, Clemson University
CPSC 6430 Machine Learning, Clemson University
International Conference on Machine Learning (ICML)
Conference on Neural Information Processing Systems (NeurIPS)
International Conference on Artificial Intelligence and Statistics (AISTATS)
The AAAI Conference on Artificial Intelligence (AAAI)
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
Medical Image Computing and Computer Assisted Interventions (MICCAI)
Annual Meeting of the Association for Computational Linguistics (ACL)
International Joint Conference on Artificial Intelligence (IJCAI)
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)
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)
A Mathematical Introduction to Compressive Sensing (Foucart, Simon, et al)
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)
Lectures on Convex Optimization 2nd Edition (Yurii Nesterov)
First-order and Stochastic Optimization Methods for Machine Learning (Guanghui Lan)
Kai has a keen interest in traditional Chinese culture including calligraphy, painting, history, poems, etc. He likes hiking and playing Chinese Chess during his leisure time, but his favorite is reading, especially some biographies such as: Lust for Life, The Gay Genius, etc.
He loves badminton and tried half and full Marathon in 2017, 2018 respectively. Besides, he has been a Madridista for 10+ years.