Events
DMS Statistics and Data Science Seminar |
| Time: Nov 05, 2020 (02:00 PM) |
| Location: ZOOM |
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Details: Speaker: Yang Zhou (Computer Science and Software Engineering, Auburn University) Title: Adversarial Machine Learning for Robust Prediction
Abstract: With continued advances in science and technology, digital data have grown at an astonishing rate in various domains and forms, such as business, geography, health, multimedia, network, text, and web data. Designing and developing machine learning algorithms that are robust against missing data, incomplete observation, or errors in data collection is essential in many real-world applications. Despite achieving remarkable performance, machine learning models, especially deep learning models, suffer from harassment caused by small adversarial perturbations injected by malicious parties and users. Given the need to understand the vulnerability and resilience of machine learning, two questions arise: (1) How to develop effective modification "attack" strategies to tamper with intrinsic characteristics of data by injecting fake information? and (2) How to develop defense strategies to offer sufficient protection to machine learning models against adversarial attacks? In this talk, I will introduce problems, challenges, and solutions for characterizing and understanding and learning vulnerability and resilience of machine learning under adversarial attacks. I will also discuss our recent work on adversarial learning over network and text data. I will conclude the talk by sketching interesting future directions for adversarial machine learning.
Modality and Time: Presenter and participants on Zoom, Thursday (Nov 5), 2pm-3pm (CST). Seminar website: http://webhome.auburn.edu/~ezc0066/stat-datasci-seminar.html
Join from PC, Mac, Linux, iOS or Android: https://auburn.zoom.us/j/93758346031 —————————————————————————————————————————————————————————— More details on the Zoom meeting.
Join from PC, Mac, Linux, iOS or Android: https://auburn.zoom.us/j/93758346031 Connect using Computer/Device audio if possible.
Or Telephone: Meeting ID: 937 5834 6031 Dial: +1 646 876 9923 (US Toll) or +1 301 715 8592 (US Toll)
Or an H.323/SIP room system: H.323: 162.255.37.11 (US West) or 162.255.36.11 (US East) Meeting ID: 937 5834 6031 |