Keynote Speech



Zhi-Hua Zhou
Professor
Keynote Title:

Some Progress and Perspectives of Machine Learning

Abstract:

In this talk, we will briefly introduce some progress of machine learning, and discuss on some future perspectives. In particular, we will introduce some recent progress of deep learning based on forests rather than neural networks. Then, we will talk about challenges and opportunities introduced by open environment machine learning tasks, and advocate to explore the form of learnware.

Brief Biography:

Zhi-Hua Zhou is a Professor and Founding Director of the LAMDA Group at Nanjing University. His main research interests are in artificial intelligence, machine learning and data mining. He authored the books "Ensemble Methods: Foundations and Algorithms" and "Machine Learning (in Chinese)" which has been reprinted for 18 times, and published more than 150 papers in top-tier international journals/conferences. According to Google Scholar, his publications have received more than 27,000 citations, with an H-index of 79. He also holds 18 patents and has good experiences in industrial applications. He has received various awards, including the National Natural Science Award of China, PAKDD Distinguished Contribution Award, IEEE ICDM Outstanding Service Award, IEEE CIS Outstanding Early Career Award, Microsoft Professorship Award, etc. He serves as the Executive Editor-in-Chief of Frontiers of Computer Science, Associate Editor-in-Chief of Science China: Information Science, and Associate Editor of ACM TIST, IEEE TNNLS, etc. He founded ACML (Asian Conference on Machine Learning) and served as General co-chair of IEEE ICDM 2016, Program co-chair of IJCAI 2015 Machine Learning track, etc. He will serve as Program co-chair of AAAI 2019. He also serves as the Chair of the CCF-AI, and was Chair of the IEEE CIS Data Mining Technical Committee. He is a Fellow of the ACM, AAAI, AAAS, IEEE, IAPR and CCF.



Geoff Webb
Professor
Keynote Title:

Learning from non-stationary distributions

Abstract:

The world is dynamic – in a constant state of flux – but most learned models are static. Models learned from historical data are likely to decline in accuracy over time. This talk presents theoretical tools for analyzing non-stationary distributions and some insights that they provide. Shortcomings of standard approaches to learning from non-stationary distributions are discussed together with strategies for developing more effective techniques.

Brief Biography:

Geoff Webb is a Professor of Information Technology Research in the Faculty of Information Technology at Monash University, where he heads the Centre for Data Science. His primary research areas are machine learning, data mining, user modelling and computational structural biology. Many of his learning algorithms are included in the widely-used Weka machine learning workbench and a commercial implementation of his association discovery techniques, Magnum Opus, is widely used. He is co-editor of the Springer Encyclopedia of Machine Learning, a member of the advisory board of Statistical Analysis and Data Mining, a member of the editorial board of Machine Learning, was editor-in-chief of Data Mining and Knowledge Discovery and was a foundation member of the editorial board of ACM Transactions on Knowledge Discovery from Data. He is PC Co-Chair of the 2015 ACM SIGKDD International Conference on Knowledge Discovery from Data, was PC Co-Chair of the 2010 IEEE International Conference on Data Mining and General Co-Chair of the 2012 IEEE International Conference on Data Mining. He is a technical advisor to BigML. He is an IEEE Fellow and has received the 2013 IEEE ICDM Service Award, the 2014 Australian Research Council Discovery Outstanding Researcher Award, the 2016 Australian Computer Society's ICT Researcher of the Year Award, and the 2016 Australasian Artificial Intelligence Distinguished Research Contributions Award.