Tsinghua Science and Technology

ISSN 1007-0214 e-ISSN 1878-7606 CN 11-3745/N
Editor-in-Chief: Jiaguang SUN
Open Access
Journal Home > Notice List > CFP–Special Issue on Machine Learning and Optimization
Release Time:2024-04-25 Views:160
CFP–Special Issue on Machine Learning and Optimization


Machine learning and optimization are both important in the field of computer science. The interplay between them plays an important role and has been well studied during these years. Machine learning and optimization is a field that combines principles from both machine learning and optimization to develop algorithms and techniques for solving complex problems efficiently. The intersection of these two fields, machine learning and optimization, is particularly powerful because it allows for the development of algorithms that can learn and adapt to improve their performance over time while also optimizing their decision-making processes.

The objective of this special issue is to publish and overview recent trends in the interdisciplinary area of machine learning and optimization. To display current developments and address challenges in theory, technology, and applications emerging in this competitive field, we devote the Special Issue on Machine Learning and Optimization. This special issue is open to all and built on high-quality papers presented at the Seventh Annual Conference on Machine Learning and Optimization (MLO 2024) held in Lanzhou, China, 17–19 May 2024. The website of MLO 2024: https://math.lzu.edu.cn/MLO2024/index.htm.

The topics of interest include, but are not limited to

  • Algorithmic Game Theory
  • Algorithms and Computational Complexity
  • Combinatorial Optimization
  • Exact and Approximation Algorithms for Big Data
  • Graph Theory and Graph Algorithms
  • Mobile Computing Network
  • Optimization in Learning Theory
  • Parallel and Distributed Algorithms
  • Probabilistic Methods for Big Data
  • Scheduling
  • Social Network Analysis
  • Submodular Optimization



Authors should prepare papers in accordance with the format requirements of Tsinghua Science and Technology, with reference to the Instruction given at https://www.sciopen.com/journal/1007-0214, and submit the complete manuscript through the online manuscript submission system at https://mc03.manuscriptcentral.com/tst with manuscript type as “Special Issue on MLO 2024”.



Deadline for submissions: July 30, 2024



Prof. Xianyue Li, Lanzhou University, China. Email: lixianyue@lzu.edu.cn

Prof. Dingzhu Du, University of Texas at Dallas, USA. Email: dzdu@utdallas.edu

Prof. Lu Han, Beijing University of Posts and Telecommunications, China. Email: hl@bupt.edu.cn