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Forum on Geometric Aspects of Machine Learning and Visual Analytics: Recent Developments and Future Challenges
The primary aim of the Forum is to bring together researchers in Computer Science, Mathematics, Statistics and related areas working on geometric problems in Machine Learning with a potential impact in Data and Visual Analytics.
In the recent years, there has been signiﬁcant progress in Machine and Statistical Learning in general, the design of algorithms that extract and process information from data sets, and the mathematical understanding of the limits and capabilities of such algorithms.
In this forum we will focus on recent trends in Machine Learning that aim at understanding the geometric nature of Machine Learning problems. It has been understood that there are rather subtle geometric structures involved in complex high dimensional data sets that have to be revealed in the process of their analysis and visualization. These structures are often hidden even in the data sets that seemingly have nothing to do with geometry (such data sets are common in many Visual Analytics applications). Novel techniques, theoretical insights, algorithms and computational techniques have been developed along this lines and will be discussed in the forum.
V. Koltchinskii, School of Mathematics, Georgia Institute of Technology
M. Maggioni, Department of Mathematics, Duke University
H. Park, Division of Computational Science and Engineering, Georgia Institute of Technology
A. Varshney, Department of Computer Science, University of Maryland
October 11, Dennis A&B, Ballys Hotel: Click on the titles to see each speaker's abstract.
Session 1: Chair: Haesun Park
Session 2: Chair: Torsten Möller
1200 - 0130 Lunch break
Session 3: Chair: Vladimir Koltchinskii
Session 4: Chair: Amitabh Varshney
October 12, Traymore A, Ballys Hotel:
0900 - 1200 Report writing session: Recent Developments and Future Challenges in Visual Analytics in relation to Geometric Aspects of Machine Learning