2023 International Conference on Frontiers of Artificial Intelligence and Statistics(CFAIS)

Memo

★2023 International Conference on Frontiers of Artificial Intelligence and Statistics (CFAIS 2023)--Ei Compendex & Scopus—Call for paper  August 18-20, 2023｜Nanjing, China｜Website: www.cfais.org   ★CFAIS 2023 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 Accepted and presented papers of CFAIS 2023 will be published in the digital conference proceedings which will submitted to Ei Compendex, Scopus, CPCI, Google Scholar and other major databases for index. Excellent papers will be recommended to be published in the journal.   ★Keynote Speakers Prof. Weifeng Gao——Xidian University, China     ★Program Preview/ Program at a glance August 18: Registration + Icebreaker Reception August 19: Opening Ceremony+ KN Speech+ Technical Sessions August 20: Technical Sessions+ Half day tour/Lab tours     ★Paper Submission PDF version submit via CMT: https://cmt3.research.microsoft.com/CFAIS2023     ★CONTACT US Ms. Riva H. W. Wong Email: This email address is being protected from spambots. You need JavaScript enabled to view it.    Website: www.cfais.org     ★Topics of Interest Approximate inference Bayesian learning Business process intelligence Causal models Classification Clustering Density estimation Federated learning Gaussian processes Generalization and regularization Graphical models High-performance computation Intelligent optimization Kernel and spectral methods Large-scale optimization algorithm Logic and probability Manifolds learning Multi-agent systems Non-Bayesian models and estimation Nonparametric models No-regret learning Optimization for deep learning Privacy-preserving learning Reinforcement learning Software and applications of artificial intelligence Sparsity and compressed sensing Statistical and computational learning theory Statistical optimization Structural learning and prediction Unsupervised and semi-supervised learning