Online learning algorithms often have the issue of exhibiting poor performance during initial stages optimization procedure, which in practical applications might dissuade potential users from deploying such solutions. In this paper, we study a novel setting, namely conservative online convex optimization, are optimizing sequence loss functions under constraint that to perform at least as well ...