New Challanges in Theoretical Machine Learning: Data Dependent Concept Spaces
Maria-Florina F Balcan · Shai Ben-David · Avrim Blum · Kristiaan Pelckmans · John Shawe-Taylor

Fri Dec 12th 07:30 AM -- 06:30 PM @ Hilton: Sutcliffe A
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This workshop aims at collecting theoretical insights in the design of data-dependent learning strategies. Specifically we are interested in how far learned prediction rules may be characterized in terms of the observations themselves. This amounts to capturing how well data can be used to construct structured hypothesis spaces for risk minimization strategies - termed empirical hypothesis spaces. Classical analysis of learning algorithms requires the user to define a proper hypothesis space before seeing the data. In practice however, one often decides on the proper learning strategy or the form of the prediction rules of interest after inspection of the data. This theoretical gap constitutes exactly the scope of this workshop.

Author Information

Maria-Florina F Balcan (Georgia Tech)
Shai Ben-David (University of Waterloo)
Avrim Blum (CMU)
Kristiaan Pelckmans (Uppsala University)
John Shawe-Taylor (UCL)

John Shawe-Taylor has contributed to fields ranging from graph theory through cryptography to statistical learning theory and its applications. However, his main contributions have been in the development of the analysis and subsequent algorithmic definition of principled machine learning algorithms founded in statistical learning theory. This work has helped to drive a fundamental rebirth in the field of machine learning with the introduction of kernel methods and support vector machines, driving the mapping of these approaches onto novel domains including work in computer vision, document classification, and applications in biology and medicine focussed on brain scan, immunity and proteome analysis. He has published over 300 papers and two books that have together attracted over 60000 citations. He has also been instrumental in assembling a series of influential European Networks of Excellence. The scientific coordination of these projects has influenced a generation of researchers and promoted the widespread uptake of machine learning in both science and industry that we are currently witnessing.

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