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Projection Retrieval for Classification
Madalina Fiterau · Artur Dubrawski

Mon Dec 03 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor #None

In many applications classification systems often require in the loop human intervention. In such cases the decision process must be transparent and comprehensible simultaneously requiring minimal assumptions on the underlying data distribution. To tackle this problem, we formulate it as an axis-alligned subspacefinding task under the assumption that query specific information dictates the complementary use of the subspaces. We develop a regression-based approach called RECIP that efficiently solves this problem by finding projections that minimize a nonparametric conditional entropy estimator. Experiments show that the method is accurate in identifying the informative projections of the dataset, picking the correct ones to classify query points and facilitates visual evaluation by users.

Author Information

Madalina Fiterau (UMass Amherst)

Madalina Fiterau is an Assistant Professor at the College of College of Information and Computer Sciences at UMass Amherst, with a focus on AI/ML. Previously, she was a Postdoctoral Fellow in the Computer Science Department at Stanford University, working with Professors Chris Ré and Scott Delp in the Mobilize Center. Madalina has obtained a PhD in Machine Learning from Carnegie Mellon University in September 2015, advised by Professor Artur Dubrawski. The focus of her PhD thesis, entitled “Discovering Compact and Informative Structures through Data Partitioning”, is learning interpretable ensembles, with applicability ranging from image classification to a clinical alert prediction system. Madalina is currently expanding her research on interpretable models, in part by applying deep learning to obtain salient representations from biomedical “deep” data, including time series, text and images. Madalina is the recipient of the GE Foundation Scholar Leader Award for Central and Eastern Europe. She is the recipient of the Marr Prize for Best Paper at ICCV 2015 and of Star Research Award at the Annual Congress of the Society of Critical Care Medicine 2016. She has organized two editions of the Machine Learning for Clinical Data Analysis Workshop at NIPS, in 2013 and 2014.

Artur Dubrawski (Carnegie Mellon University)

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