Title: Reconciling Adversarial Robustness with Accuracy via Randomized Weights. Abstract: Recent years have seen a rapid growth of research on building more robust deep neural networks against adversarial examples. Among them, adversarial training has been shown to be one of the most effective approaches. To balance the robustness of adversarial examples and the accuracy of clean examples, a series of works design enhanced adversarial training methods to strike a trade-off between them with \emph{deterministic} model parameters (i.e., weights). Noting that clean and adversarial examples are highly entangled with the network weights, we propose to study such a trade-off from another perspective, by \emph{treating weights as random variables} in order to harvest the insights yielded from statistical learning theory. Inspired by recent advances of information-theoretic generalization error bound, we found that adversarial training over the randomized weight space can potentially narrow the generalization bound of both clean and adversarial data, and improve both adversarial robustness and clean accuracy simultaneously. Building upon such insights, we propose a novel adversarial training method via Taylor expansion in the hypothesis space of the randomized weights. With PGD, CW, and Auto Attacks, an extensive set of experiments demonstrate that our method further enhances adversarial training, boosting both robustness and clean accuracy.