Date: October 14, 2016
Learning from non-stationary distributions
October 14, 2016, 1:30 – 2:30 pm
Virginia Tech Research Center – Arlington, Ballston Room
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.
Bio: 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 modeling 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 and has been incorporated in the BigML machine learning platform. He was editor-in-chief of the premier data mining journal, Data Mining and Knowledge Discovery from 2005 to 2014. He is co-editor of the Springer Encyclopedia of Machine Learning, a member of the editorial advisory boards of Data Mining and Knowledge Discovery and of Statistical Analysis and Data Mining, a member of the editorial board of Machine Learning and was a foundation member of the editorial board of ACM Transactions on Knowledge Discovery from Data. He has been Program Committee Co-Chair of the two top data mining conferences, ACM SIGKDD International Conference on Knowledge Discovery from Data (2015) and the IEEE International Conference on Data Mining (2010) and General Co-Chair of the 2012 IEEE International Conference on Data Mining. He is a technical advisor to BigML, Inc. He is an IEEE Fellow and has received the 2013 IEEE ICDM Service Award and a 2014 Australian Research Council Discovery Outstanding Researcher Award.