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The First Workshop on Machine Learning, Optimization and Control (MLOC)

The First Workshop on Machine Learning, Optimization and Control


MLOC is a forum for leading researchers and industry experts on all aspects of machine intelligence and optimization, including machine learning techniques, pattern recognition, data mining, optimization theory and control techniques. In the context of this symposium, machine learning encompasses works on concurrent deep learning techniques, their associated optimization and control techniques. Given the rise of deep learning and big data analysis, the first MLOC is particularly interested in work that addresses new optimization techniques, and control tools that attempt to improve machine learning performance in big data analytics, and work towards improved industry intelligent systems.

Specific topics of interest include (but are not limited to):

  • Deep learning techniques and applications
  • Big data analysis
  • Robust face recognition
  • Data driven control
  • Cyber Security
  • Clustering and classification techniques
  • Randomised algorithm
  • Tracking
  • Data mining and knowledge discovery
  • Computer vision and image understanding

Workshop Chair

  • Prof. Lei Zhang (张磊), The Hong Kong Polytechnic University

Workshop Organizers

  • Prof. Wanquan Liu (刘万泉), Curtin University
  • Prof. Yongsheng Ou (欧勇盛), Shenzhen Institutes of Advanced Technology, CAS
  • Prof. Aiguo Wu (吴爱国), Harbin Institute of Technology, Shenzhen
  • Prof. Chao Xu (许超), Zhejiang University

Workshop Secretaries

  • Zhiyang Wang
  • Shichao Zhou (周世超), 595689102@qq.com


2018年7月9日 至宾馆报道

8:45 早餐后,离开宾馆,前去会场
9:25 会议开始
9:30 First Keynote

  • Title: TBA (on deep learning, optimization and control in general review)
  • Spekaer: Prof. Xiangchu Feng, Xidian University

10:15 Questions and Discussions
10:45 Second Keynote

  • Title: Implementing the ADMM to big datasets: A case study of LASSO
  • Speaker: Prof. Xiaoming Yuan, Hongkong University

11:30 Questions and Discussions
12:00 午餐
14:00 Third Keynote

  • Title: Machine learning via Wasserstein statistical manifold
  • Speaker: Prof. Wuchen Li, UCLA

14:45 Questions and Discussions
15:15 Fourth Keynote

  • Title: TBA
  • Prof. Lei Zhang, The Hong Kong Polytechnic University

16:00 Questions and Discussions
17:00 离开会场,赴宾馆参加晚宴

8:30 早餐后,离开宾馆,前去会场

  • 9:00 Title: TBA (on dictionary learning) | Speaker: Yong Xu, Harbin Institute of Technology (Shenzhen)
  • 9:30 人工智能中的优化问题研究 | Lingchen Kong, Beijing Jiaotong University
  • 10:00 Title: Cardiac image quantification and motion analysis based on deep neural network | Wufeng Xue, Shenzhen University
  • 10:30 Title: TBA | Daoqiang Zhang (Tony), NUAA
  • 11:00 Title: Computer image and image understanding | Wei Xie, South China University of Technology
  • 11:30 会议结束
  • 12:00 午餐


Title: Machine learning via Wasserstein statistical manifold

Abstract: In this talk, I start with reviewing several primal-dual structures in optimal transport (Wasserstein metric). Based on it, I will introduce the Wasserstein natural gradient in parametric statistical models. We pull back the L2-Wasserstein metric tensor in probability density space to parameter space, under which the parameter space become a Riemannian manifold. The gradient and Hamiltonian flows in parameter space are derived. When parameterized densities lie in 1D, we show that the induced metric tensor and gradient flow establish explicit formulas. Examples are presented to demonstrate its effectiveness in several machine learning problems.

Title: TBA

Abstract: Dictionary learning has played an important role in machine learning. It provides concise and effective representations for patterns. We proposed novel profiles based dictionary learning, which exploits the neighbor relationship between atoms to define locality constraint. The method is constructed on the foundation that an atom is closely associated with a profile and a pair of atom and profile has identical geometry structure. The method can effectively resist noise and obtains very good performance for face recognition.

Yong Xu received the B.S. and M.S. degrees in 1994 and 1997, respectively, and the Ph.D. degree in pattern recognition and intelligence system from the Nanjing University of Science and Technology, Nanjing, China, in 2005. From 2005 to 2007, he was a Post-Doctoral Fellow with Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China.

Currently, he is a Professor with the Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen Campus, Shenzhen, China. He is the author of more than 100 scientific papers on pattern recognition and computer vision. His journal papers have been cited more than 6000 times on Google Scholar. He serves as an associate Editor-in-Chief of the CAAI Transactions on Intelligence Technology, and a senior member of IEEE. His current research interests include pattern recognition, biometrics, bioinformatics, machine learning, image processing, and video analysis.

Title: 人工智能中的优化问题研究

Abstract: 如何加强优化基础研究使得“提高学习速度,增强推断能力,降低资源消耗”是机器学习亟待解决的问题,传统的优化理论与算法面临极大挑战。目前许多国际优化专家正以极大兴趣开展此项研究.根据国家重大需求和科学前沿,为了促进最优化、统计学、机器学习与人工智能、数据科学的深度交叉与融合,为人工智能提供理论支撑与计算平台。我们ji将深入探讨人工智能中的优化问题研究思路和想法。

Title: Cardiac image quantification and motion analysis based on deep neural network

Wufeng Xue is an associate professor of the School of Biomedical Engineering, Shenzhen University. Before that, he was a postdoctoral research fellow in University of Western Ontario from Feb. 2016 to March 2018. He obtained his Ph.D. degree and Bachelor degree both from Xi'an Jiaotong University. He won the Young Scientist Award in MICCAI 2017. He is organizing the MICCAI 2018 LVQuan Challenge. His main research interests lie in medical image analysis and image quality assessment.

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