Advances in machine learning have led to rapid and widespread deployment of learning based inference and decision making for safety-critical applications, such as autonomous driving and security diagnostics. Current machine learning systems, however, assume that training and test data follow the same, or similar, distributions, and do not consider active adversaries manipulating either distribution. Recent work has demonstrated that motivated adversaries can circumvent anomaly detection or other machine learning models at test time through evasion attacks, or can inject well-crafted malicious instances into training data to induce errors in inference time through poisoning attacks. In this talk, I will describe my recent research about security and privacy problems in machine learning systems, with a focus on potential certifiably defense approaches via logic reasoning and domain knowledge integration with neural networks. We will also discuss other defense principles towards developing practical robust learning systems with robustness guarantees.

Zoom meeting link: https://newcastleuniversity.zoom.us/j/81238177624?pwd=Nm16blNtakgwMmgrVVZpbmNCU2t5Zz09

Meeting ID: 812 3817 7624
Passcode: 485647

Youtube live streaming: https://youtu.be/V5w45Im_O74

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