a collection of projects and ideas
Machine Learning
Conference Papers
Learning a Distance Metric from a Network
Blake Shaw, Bert Huang, Tony Jebara
Neural Information Processing Systems, NIPS, To appear December 2011.
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Many real-world networks are described by both connectivity information and features for every node. To better model and understand these networks, we present structure preserving metric learning (SPML), an algorithm for learning a Mahalanobis distance metric from a network such that the learned distances are tied to the inherent connectivity structure of the network.
Structure Preserving Embedding
Blake Shaw, Tony Jebara
International Conference on Machine Learning, ICML, June 2009.
Best Paper Award Winner
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View Talk: videolectures.net | MP4
Structure Preserving Embedding (SPE) is an algorithm for embedding graphs in Euclidean space such that the embedding is low-dimensional and preserves the global topological properties of the input graph. Topology is preserved if a connectivity algorithm, such as k-nearest neighbors, can easily recover the edges of the input graph from only the coordinates of the nodes after embedding.
Minimum Volume Embedding
Blake Shaw, Tony Jebara
Artificial Intelligence and Statistics, AISTATS, March 2007.
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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.
Workshop Papers
Learning a Degree-Augmented Distance Metric from a Network
Bert Huang, Blake Shaw, Tony Jebara
Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity. NIPS 2011 workshop.
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Visualizing Social Networks with Structure Preserving Embedding
Blake Shaw, Tony Jebara
Interdisciplinary Workshop on Information and Decision in Social Networks 2011
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Network Prediction with Degree Distributional Metric Learning
Bert Huang, Blake Shaw, Tony Jebara
Interdisciplinary Workshop on Information and Decision in Social Networks 2011
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Dimensionality Reduction, Clustering, and PlaceRank Applied to Spatiotemporal Flow Data
Blake Shaw, Tony Jebara
New York Academy of Science - Machine Learning Symposium 2009.
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Visualizing Graphs with Structure Preserving Embedding
Blake Shaw, Tony Jebara
Analyzing Graphs: Theory and Applications. NIPS Workshop. December 2008.
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Graph Embedding with Global Structure Preserving Constraints
Blake Shaw, Tony Jebara
New York Academy of Science - Machine Learning Symposium, October 2008.
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Minimum Volume Embedding (NYAS)
New York Academy of Science - Machine Learning Symposium 2007.
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Optimizing Eigengaps and Spectral Functions using Iterated SDP
Tony Jebara, Blake Shaw, Andrew Howard -- Learning Workshop 2007.
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B-matching for Embedding
Tony Jebara, Blake Shaw, Vlad Schogolev
Snowbird Machine Learning Conference, April 2006.
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Teaching
Programming Languages (Matlab)
w3101 section 1 - Spring 2008
Projects at Sense Networks
CabSense - The Smartest Way to Hail a Cab in NY
MacroSense - Relevant Recommendation, Personalization and Discovery from Mobile Location Data
CitySense - Live San Francisco Nightlife Activity
Patents
12/134,634 (pending) - System and Method of Performing Location Analytics
12/241,266 (pending) - Event Identification in Sensor Analytics
2/241,227 (pending) - Comparing Spatial-Temporal Trails in Location Analytics
I am currently a data scientist at Foursquare applying machine learning algorithms to large spatiotemporal datasets. I recently completed my PhD with advisor Tony Jebara at Columbia University.
Research Interests: visualization, dimensionality reduction, spatiotemporal data, networks, spectral optimizations, semidefinite programming, data mining, graph algorithms, large datasets
