Stochastics & Statistics Seminar – Shivani Agarwal (Indian Institute of Science/Radcliffe Institute, Harvard University)
March 4, 2016 | 11-12pm | 32-123

ABSTRACT: While simple supervised learning problems like binary classification and regression are fairly well understood, increasingly, many applications involve more complex learning problems: more complex label and prediction spaces, more complex loss structures, or both. The first part of the talk will discuss recent advances in our understanding of such problems, including the notion of convex calibration dimension of a loss function, unified approaches for designing convex calibrated surrogates for arbitrary losses, and connections between supervised learning and property elicitation.

The second part of the talk will focus on ranking and choice models. Specifically, I will describe some of our recent work on bringing together topic modeling tools from machine learning and statistics and choice modeling tools from marketing and econometrics to develop methods for automatically discovering topics or groups of similar items from choice data. I will describe results of applying the methods to real survey data on choices among vacation destinations, and among undergraduate concentrations at Harvard.

BIO: Shivani Agarwal is the 2015-16 William and Flora Hewlett Foundation Fellow at the Radcliffe Institute for Advanced Study at Harvard University, where she is on leave from her position as Assistant Professor and Ramanujan Fellow at the Indian Institute of Science. She leads the Machine Learning and Learning Theory Group at the Indian Institute of Science and co-directed the Indo-US Joint Center for Advanced Research in Machine Learning, Game Theory and Optimization, and is an Associate of the Indian Academy of Sciences and of the International Center for Theoretical Sciences. Prior to the Indian Institute of Science, she taught at MIT as a postdoctoral lecturer. She received her PhD in computer science at the University of Illinois, Urbana-Champaign, and her bachelors degrees in computer science and mathematics as a Nehru Scholar at Trinity College, University of Cambridge and at St. Stephen’s College, University of Delhi. Her research interests include foundational questions in machine learning, applications of machine learning in the life sciences, and connections between machine learning and other disciplines such as economics, operations research, and psychology.

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