  From the ad network standpoint, a user's activity is a multi-type sequence of
temporal events consisting of event types and time intervals. Understanding
user patterns in ad networks has received increasing attention from the machine
learning community. Particularly, the problems of fraud detection, Conversion
Rate (CVR), and Click-Through Rate (CTR) prediction are of interest. However,
the class imbalance between major and minor classes in these tasks can bias a
machine learning model leading to poor performance. This study proposes using
two multi-type (continuous and discrete) training approaches for GANs to deal
with the limitations of traditional GANs in passing the gradient updates for
discrete tokens. First, we used the Reinforcement Learning (RL)-based training
approach and then, an approximation of the multinomial distribution
parameterized in terms of the softmax function (Gumble-Softmax). Our extensive
experiments based on synthetic data have shown the trained generator can
generate sequences with desired properties measured by multiple criteria.
