Tue 25 Jun 2019 15:00 - 15:20 at 224AB - Reasoning and Optimizing ML Models Chair(s): Martin Maas

In recent years, the notion of local robustness (or robustness for short) has emerged as a desirable property of deep neural networks. Intuitively, robustness means that small perturbations to an input do not cause the network to perform misclassifications. In this paper, we present a novel algorithm for verifying robustness properties of neural networks. Our method synergistically combines gradient-based optimization methods for counterexample search with abstraction-based proof search to obtain a sound and (δ -)complete decision procedure. Our method also employs a data-driven approach to learn a verification policy that guides abstract interpretation during proof search. We have implemented the proposed approach in a tool called Charon and experimentally evaluated it on hundreds of benchmarks. Our experiments show that the proposed approach significantly outperforms three state-of-the-art tools, namely AI^2, Reluplex, and Reluval.

Tue 25 Jun
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14:00 - 15:30: Reasoning and Optimizing ML ModelsPLDI Research Papers at 224AB
Chair(s): Martin MaasGoogle
14:00 - 14:20
Talk
PLDI Research Papers
He ZhuRutgers University, USA, Zikang XiongPurdue University, Stephen Magill, Suresh JagannathanPurdue University
Media Attached
14:20 - 14:40
Talk
PLDI Research Papers
Wen-Chuan LeePurdue University, Peng LiuPurdue University, Yingqi LiuPurdue University, USA, Shiqing MaPurdue University, USA, Xiangyu ZhangPurdue University
14:40 - 15:00
Talk
PLDI Research Papers
Hui GuanNorth Carolina State University, Xipeng ShenNorth Carolina State University, Seung-Hwan LimOak Ridge National Laboratory, USA
Media Attached File Attached
15:00 - 15:20
Talk
PLDI Research Papers
Greg AndersonUniversity of Texas at Austin, USA, Shankara PailoorUniversity of Texas at Austin, USA, Isil DilligUT Austin, Swarat ChaudhuriRice University
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