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CFAIS 2020 : 2020 International Conference on Frontiers of Artificial Intelligence and Statistics (CFAIS 2020)


When Jul 20, 2020 - Jul 22, 2020
Where Seoul, South Korea
Submission Deadline Jun 20, 2020
Notification Due Jun 20, 2020
Final Version Due Jun 30, 2020
Categories    artificial intelligence   artificial intelligence

Call For Papers

★2020 International Conference on Frontiers of Artificial Intelligence and Statistics (CFAIS 2020)--Ei Compendex & Scopus—Call for paper 
July 20-22, 2020|Seoul, South Korea|Website:

★Venue: Shilla Stay Seocho, Seoul, South Korea

★CFAIS 2020 welcomes researchers, engineers, scientists and industry professionals to an open forum where advances in the field of Artificial Intelligence and Statistics can be shared and examined. The conference is an ideal platform for keeping up with advances and changes to a consistently morphing field. Leading researchers and industry experts from around the globe will be presenting the latest studies through papers and oral presentations. 

★Publication and Indexing
All accepted papers will be published in the digital conference proceedings which will be sent to be Indexed by  all major citation databases such as Ei Compendex, SCOPUS, Google Scholar, Cambridge Scientific Abstracts (CSA), Inspec, SCImago Journal & Country Rank (SJR), EBSCO, CrossRef,  Thomson Reuters (WoS), etc. 
A selection of papers will be recommended to be published in international journals.

★Keynote Speakers
Prof. Sergei Gorlatch, University of Muenster, Germany
Prof. Jingbo Zhu, Northeastern University, China

★Program Preview/ Program at a glance
July 20: Registration + Icebreaker Reception
July 21: Opening Ceremony+ KN Speech+ Technical Sessions
July 22: Technical Sessions+ Half day tour/Lab tours

★Paper Submission
1.PDF version submit via CMT:
2.Submit Via email directly to:

Ms. Betty Y. Chan

Call for papers(
Algorithms and architectures for high-performance computation
Manifolds and embedding
Approximate inference
Multi-agent systems
Bayesian models and estimation
No-regret learning
Business Process Intelligence
Non-Bayesian models and estimation
Nonparametric models
Reinforcement learning, planning, control
Deep learning including optimization
Relational learning
Density estimation
Software for and applications of AI and statistics
Game theory
Solicited topics
Gaussian processes
Sparsity and compressed sensing
Generalization and architectures
Statistical and computational learning theory
Graphical models
Structured prediction
Including learning with privacy and fairness
Topic models
Interpretability and robustness
Trustworthy learning
Kernel methods
Unsupervised and semi-supervised learning
Logic and probability

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