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SUMMARY:ASSET Seminar: "Algorithmic Stability for Trustworthy Machine Learning and Statistics"
DESCRIPTION:Abstract: \nData-driven systems hold immense potential to positively impact society\, but their reliability remains a challenge. Their outputs are often too brittle to changes in their training data\, leaving them vulnerable to data poisoning attacks\, prone to leaking sensitive information\, or susceptible to overfitting. Establishing fundamental principles for designing algorithms that are both stable—to mitigate these risks—and efficient in their use of resources is essential for enabling trustworthy data-driven systems. \nIn this talk\, I will focus on statistical estimation under differential privacy—a rigorous framework that ensures data-driven system outputs do not reveal sensitive information about individuals in their input. I will present algorithmic techniques that take advantage of beneficial structure in the data to achieve optimal error for several multivariate tasks without requiring any prior information about the data\, by building on robustness against data poisoning attacks. Lastly\, I will highlight the deeper connection between differential privacy and robustness that underpins these results. \nZoom Link (if unable to attend in-person): https://upenn.zoom.us/j/94645455801
URL:https://seasevents.nmsdev7.com/event/asset-seminar-lydia-zakynthinou-uc-berkeley/
LOCATION:Amy Gutmann Hall\, Room 414\, 3333 Chestnut Street\, Philadelphia\, 19104\, United States
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