ABSTRACT:

Robustness of neural networks have become an important issue for mission-critical applications, including self driving cars and control systems. It is thus important to verify the safety of neural networks and give provable guarantees. In this talk, I will present simple and efficient neural network verification algorithms developed by our group. Furthermore, I will discuss some effective ways to improve the robustness of neural networks.

Cho-Jui Hsieh is an assistant professor in UCLA CS. His research focus is on efficiency and robustness of machine learning systems. Cho-Jui obtained his master degree in 2009 from National Taiwan University (advisor: Chih-Jen Lin) and Ph.D. from University of Texas at Austin in 2015 (advisor: Inderjit S. Dhillon). He is the recipient of IBM Ph.D. fellowships in 2013-2015, the best paper award in KDD 2010, ICDM 2012, ICPP 2018 and best paper finalist in AISec 2017.

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*Time and place: Tuesday 2pm, Royce 156

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