Commonly used classification algorithms in machine learning, such as support vector machines, minimize a convex surrogate loss on training examples. In practice, these algorithms are surprisingly robusttoerrors inthe training data.

Kernel density estimation (KDE) is integral to a range of generative and discriminative tasks in machine learning.

Takinganinstance segmentation dataset, LVIS[7],forexample, the number of instances inbanana class can be thousands of times more than that of abait class.

Thus, we did not create new threats to society. Moreover, our work provides a new perspective on backdoor defense, as it is the first to address the certification of backdoor detection. This assumption holds in general in practice. In our setting, this is reflected by a small samplewise local probability for the labeled class for most samples used for computing LDP, which may easily lead to a large LDP . In the following, we show that a larger deviation of the learned decision boundary of a binary Bayesian classifier will affect its LDP .