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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.


Switching Time Optimization for Nonlinear Switched Systems

浙江大学玉泉校区智能系统与控制研究所二楼资料室, 9 a.m., July 2, 2018

Prof. Ryan Loxton, full professor in the School of Electrical Engineering, Computing, and Mathematics at Curtin University, Australia.

Abstract: Switched systems operate by switching among various different modes. Determining the optimal times at which the mode switches should occur is a fundamental problem in systems and control, with particular importance to the numerical solution of optimal control problems. This talk will discuss the switching time optimization problem for two classes of switched systems: those with time-dependent switching conditions (where the switches are directly controllable), and those with state-dependent switching conditions (where the switches occur when the system hits certain switching surfaces in the state space). It is widely believed that standard numerical optimization techniques struggle when applied to switching time optimization problems. In this talk we present new results showing that this challenge is over-stated; contrary to popular belief, switching times can in fact be optimized effectively using standard optimization methods. We verify this with a numerical example involving a switched system model for the production of 1,3-propanediol, an industrial polymer used in paints, adhesives, and lubricants.

Bio of the speaker: Ryan Loxton is a full professor in the School of Electrical Engineering, Computing, and Mathematics at Curtin University, Australia. His research focuses on developing new mathematical techniques to optimize complex processes in a wide range of applications such as mining, oil and gas, agriculture, and industrial process control. Ryan’s work has been recognized with several high-profile awards, including two prestigious, highly competitive fellowships from the Australian Research Council and the 2014 West Australian Young Scientist of the Year Award. A passionate advocate for industry engagement, Ryan has led many industry-funded research projects with companies such as Woodside Energy, Linkforce, Roy Hill Iron Ore, Vekta Automation, and Global Grain Handling Solutions. His mathematical algorithms underpin the Quantum software system (developed by Onesun Pty Ltd) for tracking, executing, and optimizing maintenance shutdowns in the resources sector. This technology was the winner of the 2017 South32 Designing for Excellence Innovation Award. Ryan is an Associate Editor for the Journal of Industrial and Management Optimization and has published over 70 papers in international journals and conference proceedings.