=Paper=
{{Paper
|id=Vol-2269/keynote2
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2269/keynote2.pdf
|volume=Vol-2269
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-2269/keynote2.pdf</pdf>
<pre>
           Keynote: An Optimal Control View of Adversarial Machine Learning


                                                              Xiaojin Zhu∗




                            Abstract
  Test-time adversarial examples, training set poisoning, re-
  ward shaping, etc.: these attacks as studied in adversarial ma-
  chine learning have one thing in common: the adversary liter-
  ally wants to control a machine learning system. In this talk,
  we will develop this connection to control theory. The result-
  ing view allows more clarity into adversarial learning, and
  opens up promising research directions.




   ∗
     X. Zhu is with the Department of Computer Science,
University of Wisconsin-Madison, WI, USA. e-mail: jer-
ryzhu@cs.wisc.edu
Copyright ⃝  c by the paper’s authors. Copying permitted for private
and academic purposes. In: Joseph Collins, Prithviraj Dasgupta,
Ranjeev Mittu (eds.): Proceedings of the AAAI Fall 2018 Sympo-
sium on Adversary-Aware Learning Techniques and Trends in Cy-
bersecurity, Arlington, VA, USA, 18-19 October, 2018, published
at http://ceur-ws.org

</pre>