a collection of projects and ideas
Machine Learning
Research Interests
visualization, dimensionality reduction, data mining, spectral optimizations, semidefinite programming
Papers
Minimum Volume Embedding
Blake Shaw, Tony Jebara -- In proceedings AI Stats ’07
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Minimum Volume Embedding (MVE) is an algorithm for non-linear dimensionality reduction that uses semidefinite programming (SDP) and matrix factorization to find a low-dimensional embedding that preserves local distances between points while representing the dataset in many fewer dimensions.
Minum Volume Embedding (NYAS)
New York Academy of Science - Machine Learning Symposium 2007
Optimizing Eigengaps and Spectral Functions using Iterated SDP
Tony Jebara, Blake Shaw, Andrew Howard -- Learning Workshop 2007
B-matching for Embedding
Tony Jebra, Blake Shaw, Vlad Schogolev
Snowbird Machine Learning Conference, April 2006
Teaching
Programming Languages (Matlab)
w3101 section 1 - Spring 2008
