Hi all,

the next tea-talk will be given by Prof. Brian Ziebart from the University of Illinois at Chicago.

The talk will be held at 13:45 & AA6214.

If you would like to chat with Brian, please fill your names and available time-slots on the below spreadsheet:

https://docs.google.com/spreadsheets/d/14PpRI2WYl_zPlLPGC5SPQu4fFbJDFHIIXt4oaqFdROc/edit?usp=sharing

Title: Supervised Machine Learning as an Adversarial Game

Abstract: A standard approach to supervised machine learning is to choose the form of a predictor and to then optimize its parameters based on training data. Approximations of the predictor's performance measure are often required to make this optimization problem tractable. Instead of approximating the performance measure and using the exact training data, this talk explores adversarial approximations of the training data while optimizing the exact performance measure. This formulation ensures stronger theoretical guarantees (e.g., Fisher consistency) than other surrogate loss methods, better aligns predictor construction with univariate and multivariate performance measures found in computer vision, information retrieval, and rank learning tasks, and provides flexibility for addressing sample selection bias for improved active learning.

Bio: Brian Ziebart is an Assistant Professor in the Department of Computer Science at the University of Illinois at Chicago. He earned his PhD in Machine Learning from Carnegie Mellon University where he was also a postdoctoral fellow. His interests lie in the intersections between machine learning, game theory, and decision theory. He has published over 25 articles in leading machine learning and artificial intelligence venues, including one that received a Best Paper award at the International Conference on Machine Learning. 

Best,

--Junyoung