Inthis paper,we provide ageneralization error bound applicable for graph embedding both in linear and hyperbolic spaces under various negative sampling settings that appear in graph embedding.

Note f = , so havethat / f =1.

We study a generalization of boosting to the multiclass setting. We introduce a weak learning condition for multiclass classification that captures the original notion ofweak learnability asbeing "slightly better than random guessing".

Fairness and robustness are critical elements of Trustworthy AI that need to be addressedtogether.

However, much less effort has been devoted to exploring the adversarial robustness of pre-trained models.