Lecture Title: AI, Information, and the Future of Machine Learning
Presented By: Jeffrey A. Bilmes, Professor, Department of Electrical Engineering, The University of Washington
Location: Bahen Centre, 40 St. George Street, Room 1180
Abstract: Machine learning involves the extraction and aggregation of information from data. The ability to extract useful information from increasingly larger datasets, however, is becoming decreasingly cost-effective. This is because data is getting bigger at a rate that computational improvements are becoming more expensive to continue to match. A common strategy to overcome such difficulties is either to discard data or to randomly subsample, but this is not sustainable if machine learning is to continue to improve by exploiting all useful information in available data. In this talk, we will discuss how to be more efficient in representing information in data through the process of summarization. In particular, we will see how submodular and supermodular functions can model information in data, and how these can be used to produce theoretically justified but still practical algorithms for various forms of data summarization. This will include approaches that summarize data before training takes place, and also some new tactics that learn and summarize simultaneously.
Biography: Jeffrey A. Bilmes is a professor at the Department of Electrical Engineering at the University of Washington, Seattle Washington. He is also an adjunct professor in Computer Science & Engineering and the department of Linguistics. Prof. Bilmes is the founder of the MELODI (MachinE Learning for Optimization and Data Interpretation) lab here in the department. Bilmes received his Ph.D. from the Computer Science Division of the department of Electrical Engineering and Computer Science, University of California in Berkeley. He was also a researcher at the International Computer Science Institute, and a member of the Realization group there.
Prof. Bilmes is a 2001 NSF Career award winner, a 2002 CRA Digital Government Fellow, a 2008 NAE Gilbreth Lectureship award recipient, and a 2012/2013 ISCA Distinguished Lecturer. Prof. Bilmes was a UAI (Conference on Uncertainty in Artificial Intelligence) program chair (2009) and then the general chair (2010). He was also a workshop chair (2011) and the tutorials chair (2014) at NIPS (Neural Information Processing Systems). He is currently an action editor for JMLR (Journal of Machine Learning Research).
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The Department of Computer Science Distinguished Lecture Series is named the “C.C. ‘Kelly’ Gotlieb Distinguished Lecture Series” in honour of the late Professor Emeritus (1921-2016) and first department chair (1964-68) who is widely regarded as the "Father of Computing in Canada".This series promotes distinguished scholarship in the field.