With email traffic increasing, leading Web mail services have started to offer features that assist users in reading and processing their in- boxes. One approach is to identify “important” messages, while a complementary one is to bundle messages, especially machine- generated ones, in pre-defined categories. We rather propose here to go back to the task at hand and consider what actions the users might conduct on received messages. We thoroughly studied, in a privacy-preserving manner, the actions of a large number of users in Yahoo mail, and found out that the most frequent actions are typ- ically read, reply, delete and a sub-type of delete, delete-without- read. We devised a learning framework for predicting these four actions, for users with various levels of activity per action. Our framework leverages both vertical learning for personalization and horizontal learning for regularization purposes. In order to ver- ify the quality of our predictions, we conducted a large-scale ex- periment involving users who had previously agreed to participate in such research studies. Our results show that, for recall values of 90%, we can predict important actions such as read or reply at precision levels up to 40% for active users, which we consider pretty encouraging for an assistance task. For less active users, we show that our regularization achieves an increase in AUC of close to 50%. To the best of our knowledge, our work is the first to provide a unified framework of this scale for predicting multiple actions on Web email, which hopefully provides a new ground for inventing new user experiences to help users process their inboxes.