Recent Advances in Statistical Learning: Theories, Technologies and Environmental & Industrial Applications

Dear Colleagues,

In recent years, the outstanding renaissance of artificial intelligence (AI) has achieved unprecedented success in all fields across  the world. Meanwhile, innovative applications in natural language processing (NLP), computer vision (CV), and recommender systems (RS) have demonstrated the superior learning power of AI in learning effective patterns from datasets. In fact, the foundation of AI is heavily reliant on statistical learning models and theories. Statistical learning from big data or sparse data can be very helpful to investigate intelligent systems' characteristics and guarantee their stability, security, and economic viability.

However, there are still many challenging problems in the statistical learning field that remain unsolved due to the various data formats, more complex data structures, and high sparsity of the data.  Thus, new techniques and advanced engineering applications in statistical learning are still appealing to many scholars in the field.

This Special Issue aims to provide selected contributions on recent advances in statistical learning theories, models, and applications. The potential topics include, but not limited to, the following:

• Statistical learning and data driven control intelligent systems;
• Deep learning theory and applications;
• Statistical learning in linear and nonlinear systems;
• Unsupervised statistical learning;
• Deep generative models and related applications;
• Computer vision;
• Natural language processing;
• Deep reinforcement learning.

Dr. Yusen He
Dr. Huajin Li
Dr. Xun Shen
Prof. Dr. Zhenhao Tang
Guest Editors

Manuscript Submission Information

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