[Lisa_seminaires] Brian Ziebart on "Supervised Machine Learning as an Adversarial Game", March 31, 13:45, AA6214

Junyoung Chung elecegg at gmail.com
Jeu 30 Mar 14:27:53 EDT 2017


Hi all,

the room is changed to AA5340.

Best,

On Sun, Mar 26, 2017 at 10:00 PM Junyoung Chung <elecegg at gmail.com> wrote:

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

-- 
--Junyoung
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