am currently an algorithm
engineer at Alibaba Group (U.S.) Inc. Before I joined Alibaba Group, I
was a Reseach Investigator in the Medical School of
Michigan University advised by Dr. Jieping Ye until April 2019. I
worked in the School
of Computer Science at Carnegie Mellon University from July 2014 to Sep
2015 advised by Dr. Alexander G. Hauptmann. I received my Ph.D. degree
in computer science from Tsinghua University in 2014 under the
supervision of Prof. Chuangshui Zhang. During my Ph.D. study, I had
been a visiting scholar in Michigan State University advised by Prof.
Rong Jin from Nov 2012 to Dec 2013 and in CMU from Dec 2013 to July
2014. My research interest mainly focuses on high dimensional high
order statistics and efficient deep learning. Here is my CV.
Ming Lin, Shuang Qiu, Jieping Ye, Xiaomin Song, Qi Qian, Liang Sun, Shenghuo Zhu, Rong Jin. Which Factorization Machine Modeling is Better: A Theoretical Answer with Optimal Guarantee. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019. [arxiv]
Tieliang Gong, Guangtao Wang, Jieping Ye, Zongben Xu, Ming Lin. Margin Based PU Learning. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 2018. [pdf] [appendix] [Github Code]
Xiang Li, Aoxiao Zhong, Ming Lin, Ning Guo, Mu Sun, Arkadiusz Sitek, Jieping Ye, James Thrall, Quanzheng Li. Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis. In Proceedings of the 8th International Workshop on Machine Learning in Medical Imaging (MLMI), 2017.
Zhenzhong Lan, Shoou-I Yu, Dezhong Yao, Ming Lin, Bhiksha Raj ; Alexander Hauptmann. The Best of BothWorlds: Combining Data-Independent and Data-Driven Approaches for Action Recognition. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Pages 1196-1205, 2016.
Lin, Jieping Ye. A Non-convex One-Pass Framework for Generalized Factorization
Machine and Rank-One Matrix Sensing. In Proceedings of the 30th Annual Conference on Neural Information Processing Systems (NIPS), Pages 1633-1641, 2016. [arXiv]
Gang, Ming Lin, Yi Yang, Gerard de Melo, Alexander G. Hauptmann.
Concepts Not Alone: Exploring Pairwise Relationships for Zero-Shot
Video Activity Recognition. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI), Pages 3487-3493, 2016.
Chuang Gang, Ming Lin, Yi Yang,
Alexander G. Hauptmann. Exploring Semantic Inter-Class Relationships
(SIR) for Zero-Shot Action Recognition. In Proceedings of the the 29th
AAAI Conference on Artificial Intelligence (AAAI), Pages 3769-3775, 2015.
Zhenzhong Lan, Ming Lin, Xuanchong Li, Alexander G.
Hauptmann, Bhiksha Raj. Beyond Gaussian Pyramid: Multi-skip Feature
Stacking for Action Recognition. IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), Page 204-212, 2015.
Lin, Zhenzhong Lang, Alexander G. Hauptmann. Density Corrected Sparse
Recovery when R.I.P. Condition is Broken. In Proceedings of the
24th International Joint Conference on Artificial Intelligence (IJCAI), Pages 3664-3670, 2015.
Yu, Lu Jiang, Zexi Mao, Xiaojun Chang, Xingzhong Du, Chuang Gan,
Zhenzhong Lan, Zhongwen Xu, Xuanchong Li, Yang Cai, Anurag Kumar, Yajie
Miao, Lara Martin, Nikolas Wolfe, Shicheng Xu, Huan Li, Ming Lin,
Zhigang Ma, Yi Yang, Deyu Meng, Shiguang Shan, Pinar Duygulu Sahin,
Susanne Burger, Florian Metze, Rita Singh, Bhiksha Raj, Teruko
Mitamura, Richard Stern, Alexander Hauptmann. Informedia@ trecvid 2014 med and mer. NIST TRECVID Video Retrieval Evaluation Workshop, 2014.
Lin, Rong Jin, Changshui Zhang. Efficient Sparse Recovery via Adaptive
Non-Convex Regularizers with Oracle Property. In Proceedings of
the 30th Conference on Uncertainty in Artificial Intelligenre (UAI), Pages 505-514, 2014. [pdf appendix]
Zhang, Jinfeng Yi, Ming Lin, Xiaofei He. Online Kernel Learning with a
Near Optimal Sparsity Bound. In Proceedings of the 30th International
Conference on Machine Learning (ICML), pages 621 – 629, 2013.
Jian Liang, Kun Chen, Ming Lin, Changshui Zhang, Fei Wang. Robust finite mixture regression for heterogeneous targets. Data Mining and Knowledge Discovery, Volume 32, Issue 6, pp 1509–1560, November 2018.
Lin, Pinghua Gong, Tao Yang, Jieping Ye, Roger L. Albin, Hiroko H.
Dodge. Big Data Analytical Approaches to the NACC Dataset: Aiding
Preclinical Trial Enrichment. Alzheimer Dis Assoc Disord. Volume 32, Issue1, Pages 18-27,
Chang, Ming Lin, Changshui Zhang. On the generalization ability of
online gradient descent algorithm under the quadratic growth condition. IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, pp. 5008-5019, Oct. 2018.
Ming Lin, Vaibhav Narayan, Wayne C. Drevets, Jieping Ye, Qingqin Li. Application of Growth Mixture Modeling in Antidepressant Treatment Response Studies. Biological Psychiatry, Volume 81, Issue 10, Supplement, Page S224, May 2017.
Xiaojun Chang, Zhigang Ma, Ming Lin, Yi Yang, Alexander G. Hauptmann. Feature Interaction Augmented Sparse Learning for Fast Kinect Motion Detection. IEEE Transactions on Image Processing, Volume 26, Issue 8, Pages 3911-3920. 2017.
Yu, Yi Yang, Zhongwen Xu, Shicheng Xu, Deyu Meng, Zexi Mao, Zhigang Ma,
Ming Lin, Xuanchong Li, Huan Li, Zhenzhong Lan, Lu Jiang, Alexander G.
Hauptmann, Chuang Gan, Xingzhong Du, Xiaojun Chang. Strategies for Searching Video Content with Text Queries or Video Examples (Invited Paper). ITE Transactions on Media Technology and Applications 4.3, Pages 227-238, 2016.
Ming Lin, Lijun Zhang, Rong
Jin, Shifeng Weng, Changshui Zhang. Online Kernel Learning with Nearly
Constant Support Vectors. Neurocomputing. Volume 179, Pages 26–36, 2016.
Hu, Ming Lin, Changshui Zhang. Dependent Online Kernel Learning with
Constant Number of Random Fourier Features. IEEE Transactions on Neural
Networks and Learning Systems (TNNLS), Volume 26, Issue 10, Pages 2464-2476, 2015.
Ming Lin, Fei Wang,
Changshui Zhang. Large-Scale Eigenvector Approximation via Hilbert
Space Embedding Nystrom. Pattern Recognition (PR), 48(5), Pages 1904-1912,
Lin, Shifeng Weng, Changshui Zhang. On the Sample Complexity of Random
Fourier Features for Online Learning: How Many Random Fourier Features
Do We Need ? . ACM Transactions on Knowledge Discovery from Data
(TKDD), Volume 8 Issue 3, Pages 13:1--13:19, June 2014, ISSN 1556-4681.
Yang, Ming Lin, Chenping Hou, Changshui Zhang, Yi Wu. A General
Framework for Transfer Sparse Subspace Learning. Neural Computing and
Applications. Volume 21, Number 7, Pages 1801-1817, 2012
Lan, Xuanchong Li, Ming Lin, Alexander G. Hauptmann. Long-short term
motion feature for action classification and retrieval. 2015. [arXiv]
Lan, Shoou-I Yu, Ming Lin, Bhiksha Raj, Alexander G. Hauptmann.
Handcrafted local features are convolutional neural networks. 2015. [arXiv]
Shuang Qiu, Tingjin Luo, Jieping Ye, Ming Lin. Nonconvex One-bit Single-label Multi-label Learning. arXiv:1703.06104 [stat.ML]. 2017. [arXiv] Ming Lin, Shuang Qiu, Bin Hong, Jieping Ye. The Second Order Linear Model. arXiv:1703.00598 [stat.ML]. 2017. [arXiv]
libSLM: A Toolbox for the Second Order Linear Model
The libSLM implements several solvers for the second order linear model proposed by our paper "The Second Order Linear Model". [arXiv]