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YINS Seminar Archives: Kamalika Chaudhuri (Mar. 3, 2021)
YINS Seminar Archives: Kamalika Chaudhuri (Mar. 3, 2021)
“Challenges in Reliable Machine Learning”
Speaker: Kamalika Chaudhuri
Associate Professor at the University of California, San Diego
As machine learning is increasingly deployed, there is a need for reliable and robust methods that go beyond simple test accuracy. In this talk, we will discuss two challenges that arise in reliable machine learning. The first is robustness to adversarial examples, that are small imperceptible perturbations to legitimate test inputs that cause machine learning classifiers to misclassify. While recent work has proposed many attacks and defenses, why exactly they arise still remains a mystery. In this talk, we’ll take a closer look at this question.
The second problem is overfitting, that many generative models are known to be prone to. Motivated by privacy concerns, we formalize a form of overfitting that we call data-copying – where the generative model memorizes and outputs training samples or small variations thereof. We provide a three sample test for detecting data-copying, and study the performance of our test on several canonical models and datasets.
This was part of the YINS Distinguished Lecturer Series and was presented on Wednesday, March 3, 2021.