Wednesday, January 25, 2017
4:00 PM -
5:00 PM
Annenberg 213
Seminar in Computing + Mathematical Sciences
A tutorial on metric learning with some recent advances
Nakul Verma,
Research Specialist,
Janelia Research Campus HHMI,
Speaker's Bio:
Dr. Nakul Verma is a research specialist at Janelia Research Campus HHMI, a center for conducting fundamental research in basic sciences, where he is developing novel statistical techniques to help biologists quantitatively analyze behavioral phenotypes in model organisms and better understand the underlying neuroscience and genetic principles. His interests include high dimensional data analysis and exploiting intrinsic structure in data to design effective learning algorithms. Previously Dr. Verma worked at Amazon as a research scientist developing risk assessment models for real-time fraud detection. Dr. Verma received his Ph.D. in Computer Science from UC San Diego specializing in Machine Learning.
Dr. Nakul Verma is a research specialist at Janelia Research Campus HHMI, a center for conducting fundamental research in basic sciences, where he is developing novel statistical techniques to help biologists quantitatively analyze behavioral phenotypes in model organisms and better understand the underlying neuroscience and genetic principles. His interests include high dimensional data analysis and exploiting intrinsic structure in data to design effective learning algorithms. Previously Dr. Verma worked at Amazon as a research scientist developing risk assessment models for real-time fraud detection. Dr. Verma received his Ph.D. in Computer Science from UC San Diego specializing in Machine Learning.
Goal of metric learning is to learn a notion of distance---or a metric---in the representation space that yields good prediction performance on data. In this tutorial we explore some classic ways one can efficiently find good metrics. Starting from the basics, we'll cover classic techniques like Mahalanobis Metric for Clustering (MMC) and Large Margin Nearest Neighbor (LMNN) and discuss key principles that make these techniques effective in improving prediction performance. We will also study some extensions and see how metric learning has helped in ranking problems (information retrieval) and large scale classification.
Event Sponsors:
For more information, please contact Sheila shull by phone at 626.395.4560 or by email at [email protected].