★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.
A selection of papers will be recommended to be published in the journal.

★Keynote Speakers
Prof. Huiyu Zhou, University of Leicester, UK

★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
1.PDF version submit via CMT: https://cmt3.research.microsoft.com/CFAIS2023

★CONTACT US
Ms. Willa P. P. Wong
Email: info@cfais.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