Title: Efficiently Meta-Learning for Robust Deep Networks without Prior Unbiased Set. Abstract: Learning with noisy labels is a practically challenging problem in robust deep learning. Recent efforts to improve the robustness are made by meta-learning the sample weights or transition matrix from a prior unbiased set. Thus, previous meta-learning based approaches generally assume the existence of such prior unbiased set. Unfortunately, this assumption unrealistically simplifies the task of learning noisy labels in real-world scenarios; even worse the updating iterations in previous meta-learning algorithms typically demand prohibitive computational cost. This paper proposes an efficient meta-learning approach for robust deep learning to address these challenges. Specifically, without relying on prior unbiased validation set, our method dynamically estimates unbiased samples in training data and leverages meta-learning to refine the deep networks. Furthermore, to significantly reduce the updating iterations in optimization cost, we elaborately design the inner loop adaption and outer loop optimization of the meta-learning paradigm, respectively. Experimental results demonstrate that our approach is able to save about 6 times training time while achieving comparable or even better generalization performance. In particular, we improve accuracy on the CIFAR100 benchmark at 40% instance-dependent noise by more than 13% in absolute accuracy.