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

David Krueger david.scott.krueger at gmail.com
Mer 29 Mar 17:17:35 EDT 2017


I think it would be useful for anyone who is interested in *covariate
shift, robustness, or adversarial examples* to speak with him!




On Wed, Mar 29, 2017 at 5:10 PM, Junyoung Chung <elecegg at gmail.com> wrote:

> Hi all, if you are interested in talking with Prof. Brian Ziebart about
> research,
> please fill in a time-slot in this spreadsheet:
> https://docs.google.com/spreadsheets/d/14PpRI2WYl_zPlLPGC5SP
> Qu4fFbJDFHIIXt4oaqFdROc/edit?usp=sharing
>
> --Junyoung
>
> 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_zPlLPGC5SP
>> Qu4fFbJDFHIIXt4oaqFdROc/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
>>
>
>
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