Recent AI advancements for frontier data science
Four main-track papers and one tutorial have been accepted and presented in the CORE A* conference, The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) held February 2–7, 2018 in New Orleans, which is also one of the most prestigious artificial intelligence conferences. All the lead authors, as shown below, are PhD students in AAI with the supervision of Professor Longbing Cao.
The papers and tutorial address significant data science challenges in various areas, including deep learning, non-IID learning, Bayesian learning, representation learning, ensemble learning, and intelligent recommender systems, with applications in different contexts such as scalable or transactional recommender systems, high-dimensional outlier detection and mixed data clustering.
- Trong Dinh Thac Do, Longbing Cao. Coupled Poisson Factorization Integrated with User/Item Metadata for Modeling Popular and Sparse Ratings in Scalable Recommendation. AAAI–18, New Orleans, Louisiana, USA.
- Guansong Pang, Longbing Cao, Ling Chen, Defu Lian and Huan Liu. Sparse Modeling-based Sequential Ensemble Learning for Effective Outlier Detection in High-dimensional Numeric Data. AAAI–18, New Orleans, Louisiana, USA.
- Shoujin Wang, Liang Hu, Longbing Cao, Xiaoshui Huang, Defu Lian and Wei Liu. Attention-based Transactional Context Embedding for Next-Item Recommendation. AAAI–18, New Orleans, Louisiana, USA.
- Songlei Jian, Liang Hu, Longbing Cao, and Kai Lu. Metric-based Auto-Instructor for Learning Mixed Data Representation. AAAI–18, New Orleans, Louisiana, USA.
- Liang Hu, Longbing Cao, Jian Cao, Songlei Jian. When Advanced Machine Learning Meets Intelligent Recommender Systems, AAAI–18 Tutorial, New Orleans, Louisiana, USA.