论文投稿


投稿指南


我们邀请作者们提交一页的摘要或者不超过12页的论文, (12页中不包括参考文献和首页(作者,隶属关系,关键字,摘要等),来介绍自己在理论计算机科学上的原创性研究工作。 所有提交的内容都必须以 LIPIcs style 样式设置格式,并通过以下链接通过Easychair提交给相应的分组:

https://easychair.org/conferences/?conf=ijtcs2020

pdflatex和LIPIcs样式的使用是强制性的:明显偏离要求格式的论文可能会被直接拒绝,而不考虑其优点。

我们不允许作者事先出版投稿,也不允许同时向其他出版物(会议或期刊)投稿。

对提交内容进行适当的科学评估所必需的技术细节,必须包含在12页的提交内容或附有清晰标签的附录中。 程序委员会成员可自行决定是否咨询这些细节。强烈鼓励作者在ArXiv,HAL,ECCC等在线平台中免费提供其提交的完整版本。

每篇被接受的论文都要求至少一位作者出席会议并发表演讲。如果没有作者参加会议,论文可以从会议记录中排除。

 

论文主题


我们接收关于理论计算机科学的原创性研究工作。主题包括但不限于:

Track A (Algorithmic Game Theory)


  • Solution Concepts in Game Theory
  • Efficiency of Equilibria and Price of Anarchy
  • Complexity Classes in Game Theory
  • Computational Aspects of Equilibria
  • Computational Aspects of Fixed-Point Theorems
  • Repeated Games and Convergence of Dynamics
  • Reputation, Recommendation and Trust Systems
  • Network Games and Graph-Theoretic Aspects of Social Networks
  • Cost-Sharing Algorithms and Analysis
  • Algorithmic Mechanism Design
  • Computing with Incentives
  • Computational Social Choice
  • Decision Theory, and Pricing
  • Auction Design and Analysis
  • Economic Aspects of Distributed Computing
  • Internet Economics and Computational Advertising


Track B (Blockchain)


  • Consensus Algorithms
  • Cryptographic Primitives
  • Decentralized Economy and Finance
  • Network Formation and Analysis
  • Smart Contract Languages
  • Formal Aanlysis
  • Theoretical Studies of Existing Blockchain Systems
  • Privacy and the Right to be Forgotten
  • Scalability
  • Blockchain Interoperbility


Track C (Multi-agent Reinforcement Learning)


  • Communications in MARL
  • Opponent Modeling
  • Cooperation and Coordination
  • Dynamics of Multi-agent Strategies
  • Evaluation of MARL Algorithms
  • Logics for Multi-agent Strategies
  • Credit Assighment
  • Game Theory for MARL
  • MARL for Robot Control
  • MARL Applications


Track D (Learning Theory)


  • Statistical Learning Theory
  • Online learning, Bandit and Reinforcement Learning
  • Theory for Deep Learning
  • Theory for Semi-supervised, Unsupervised, Transfer, Multi-task Learning
  • Optimization for Machine Learning
  • Learning and Game Theory
  • Learning and Multi-agent Systems
  • Federated Learning. Privacy, Security and Robustness in Learning


Track E (Quantum Computing)


  • Quantum Algorithms
  • Models of Quantum Computation
  • Quantum Complexity Theory
  • Quantum Cryptography
  • Quantum Communication Complexity
  • Quantum Information Theory
  • Quantum Simulation
  • Quantum Coding Theory
  • Quantum Machine Learning
  • Fault-tolerant Quantum Computing

Track F (Machine Learning and Formal Method)


    Track G (Algorithm and Complexity)