Title: An Empirical Study of the Neural Contextual Bandit Algorithms. Abstract: Recent advances in representation learning have made significant influences on solutions of contextual bandit problems. Neural bandit algorithms have been actively developed and reported to gain extraordinary performance improvement against classical bandit algorithms in numerous papers. However, there lacks a comprehensive comparison among the existing neural bandit algorithms, and it is still not clear whether or when they can succeed in complex real-world problems. In this work, we present an inclusive empirical study on three different categories of existing neural bandit algorithms on several real-world datasets. The results show that such algorithms are highly competitive against their classical counterparts in most cases, however the advantage is not consistent. The results also reveal crucial challenges for future research in neural bandit algorithms. 