Title: Certified Robustness to Adversarial Label-Flipping Attacks via Randomized Smoothing. Abstract: This paper considers label-flipping attacks, a type of data poisoning attack where an adversary relabels a small number of examples in a training set in order to degrade the performance of the resulting classifier. In this work, we propose a strategy to build classifiers that are certifiably robust against a strong variant of label-flipping, where the adversary can target each test example independently. In other words, for each test point, our classifier makes a prediction and includes a certification that its prediction would be the same had some number of training labels been changed adversarially. Our approach leverages randomized smoothing, a technique that has previously been used to guarantee test-time robustness to adversarial manipulation of the input to a classifier. Further, we obtain these certified bounds with no additional runtime cost over standard classification. On the Dogfish binary classification task from ImageNet, in the face of an adversary who is allowed to flip 10 labels to individually target each test point, the baseline undefended classifier achieves no more than 29.3% accuracy; we obtain a classifier that maintains 64.2% certified accuracy against the same adversary.