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Ming Lin (林铭)



Postdoctoral Research Fellow

School of Computer Science

Carnegie Mellon University

Email: minglin (aT) cs dot cmu . edu

Office: Wean Hall 4204

About Me




Hi! I am Ming Lin. I am a Postdoctoral Research Fellow in the School of Computer Science at Carnegie Mellon University. I'm working with 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 2014. My research interest is mainly focus on machine learning theory and its applications in computer vision. Here is my CV.




Journal Paper


1.      Zhen 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), 2015.

2.      Ming Lin, Fei Wang, Changshui Zhang. Large-Scale Eigenvector Approximation via Hilbert Space Embedding Nystrom. Pattern Recognition (PR), 48(5), pp 1904-1912, 2015.

3.      Zheng Pan, Ming Lin, Guangdong Hou, Changshui Zhang. Damping proximal coordinate descent algorithm for non-convex regularization. Neurocomputing, vol 152 pp 151-163, 2015.

4.      Ming Lin, Shifeng Weng, Changshui Zhang. On the Sample Complexity of Random Fourier Features for Online Learning. ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 8 Issue 3, Pages 13:1--13:19, June 2014.

5.      Shizhun 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, August 2012.


Conference Paper


1.      Ming Lin, Zhenzhong Lan, Alexander G. Hauptmann. Density Corrected Sparse Recovery when R.I.P. Condition is Broken. International Joint Conference on Artificial Intelligence (IJCAI), 2015. [pdf]

2.      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), 2015.

3.      Chuang Gan, Ming Lin, Yi Yang, Yueting Zhuang and Alexander G. Hauptmann. Exploring Semantic Inter-class Relationships (SIR) for Zero-shot Action Recognition. Association for the Advancement of Artificial Intelligence (AAAI), 2015.

4.      Ming Lin, Rong Jin, Changshui Zhang. Efficient Sparse Recovery via Adaptive Non-Convex Regularizers with Oracle Property. Uncertainty in Artificial Intelligenre (UAI), 2014.  [pdf] [appendix]

5.      Lijun Zhang, Jinfeng Yi, Ming Lin, Xiaofei He. Online  Kernel Learning with a Near Optimal Sparsity Bound. International Conference on Machine Learning (ICML), 2013.

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