What is AutoML?
Machine learning has achieved great successes in online advertising, recommender systems, financial market analysis, computer vision, computational linguistics, bioinformatics and many other fields. However, its success crucially relies on human machine learning experts, as human experts are involved to some extent, in all systems design stages (e.g., for selecting appropriate ML architectures and their hyperparameters). As the complexity of these tasks is often beyond non-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML.
We are organizing a workshop around AutoML collocated with the 15th Pacific Rim International Conference on Artificial Intelligence August 27-31, 2018, Nanjing, China. The workshop will feature several keynote talks by leading experts on AutoML and related fields. In addition, a poster session will be organized for presenting research progress by contributed authors(Tentative).
Organization
Adviser
- Isabelle Guyon, UPSud/INRIA Univ. Paris-Saclay, France & ChaLearn, USA, guyon@clopinet.com
Co-chairs
- Wei-Wei Tu, 4Paradigm Inc., China, tuww.cn@gmail.com
- Hugo Jair Escalante, INAOE (Mexico), ChaLearn (USA), hugo.jair@gmail.com
- Zhanxing Zhu, Peking University, China, zhanxing.zhu@pku.edu.cn
Organization Committee
- Yang Yu, LAMDA Group, Nanjing University, China, yuy@nju.edu.cn
- Yi-Qi Hu, LAMDA Group, Nanjing University, China, huyq@lamda.nju.edu.cn
About
- Previous / Related AutoML Events
- Previous AutoML Challenges: The First AutoML Challenge and The Second AutoML Challenge.
- Opening soon: The Third AutoML Challenge (Part of NIPS 2018 Competition Track)
- Microsoft research blog post on AutoML Challenge can be found here.
- KDD Nuggets post on AutoML Challenge can be found here.
- I. Guyon et al. A Brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning Without Human Intervention. ICML W 2016. link
- I. Guyon et al. Design of the 2015 ChaLearn AutoML challenge. IJCNN 2015. link
- Springer Series on Challenges in Machine Learning. link
Organization Institutions
Sponsor
