★2021 International Conference on Frontiers of Artificial Intelligence and Statistics (CFAIS 2021)--Ei Compendex & Scopus—Call for paper 
December 1-3, 2021｜Beijing, China｜Website: www.cfais.org

★CFAIS 2021 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 send to be indexed by all major citation databases such as Ei Compendex, Scopus, CPCI, 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 the International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems as special issue (Impact Factor: 1.286), which will be abstracted and indexed in: Science Citation Index Expanded (SCIE), ISI Alerting Services, CompuMath Citation Index, Current Contents/Engineering, Computing & Technology, ACM Guide to Computing Literature, Mathematical Reviews, Inspec, Zentralblatt MATH and Compendex.

★Keynote Speakers
Prof. Osvaldo N. Oliveira Jr., University of São Paulo, Brazil

★Program Preview/ Program at a glance
December 1: Registration + Icebreaker Reception
December 2: Opening Ceremony+ KN Speech+ Technical Sessions
December 3: Technical Sessions+ Half day tour/Lab tours

★Paper Submission
1.PDF version submit via CMT: https://cmt3.research.microsoft.com/CFAIS2021
2.Submit Via email directly to: cfais@hksra.org

★CONTACT US
Ms. Willa P. P. Wong
Email: cfais@hksra.org
Website: www.cfais.org

Call for papers(http://www.cfais.org/cfp.html):
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

Causality

Nonparametric models

Classification

Regression

Clustering

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