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PhD Thesis Defense - Yannan Li

PhD Thesis Defense - Yannan Li

Title: Formal Analysis of the Data Poisoning Robustness of K-Nearest Neighbors

Committee members(Lexicographic order): Pierluigi Nuzzo, Mukund Raghothaman, Chao Wang (chair)

Abstract: Data poisoning, which aims to corrupt a machine learning model and change its inference results by changing data elements in its training set, poses a significant threat to machine learning based software systems. However, formally certifying data poisoning robustness is a challenging task. I designed and implemented a set of formal methods for deciding, both efficiently and accurately, the data-poisoning robustness of the k-nearest neighbors (KNN) algorithm, which is a widely-used supervised machine learning technique. First, I developed a method for certifying the data-poisoning robustness of KNN by soundly overapproximating both the learning and inference phases of the KNN algorithm. Second, I developed a method for falsifying data-poisoning robustness, by quickly detecting the truly-non-robust cases using search space pruning and sampling. Finally, I extended these methods to other attack models and fairness certification, thus allowing for a more comprehensive analysis of the robustness of KNN.

Audiences: Everyone Is Invited

Contact: Melissa Ochoa

Event Link: https://usc.zoom.us/j/94891715635?pwd=SFI5VFBtMndhN3BORk5GSjRyS2IzQT09