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

Vincent Dumoulin dumouliv at iro.umontreal.ca
Jeu 30 Mar 11:08:16 EDT 2017


Any chance Brian can share his slides for people who won't be able to
attend?

On Wed, Mar 29, 2017 at 5:18 PM David Krueger <david.scott.krueger at gmail.com>
wrote:

> 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_zPlLPGC5SPQu4fFbJDFHIIXt4oaqFdROc/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_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
>
>
>
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