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In the maximum coverage problem we are given d subsets from a universe [n], and the goal is to output k subsets such that their union covers the largest possible number of distinct items. We present the first algorithm for maximum coverage in the turnstile streaming model, where updates which insert or delete an item from a subset come one-by-one. Notably our algorithm only uses polylogn update time. We also present turnstile streaming algorithms for targeted and general fingerprinting for risk management where the goal is to determine which features pose the greatest re-identification risk in a dataset. As part of our work, we give a result of independent interest: an algorithm to estimate the complement of the pth frequency moment of a vector for p ≥ 2. Empirical evaluation confirms the practicality of our fingerprinting algorithms demonstrating a speedup of up to 210x over prior work. 

Training with noisy labels often yields suboptimal performance, but retraining a model with its own predicted hard labels (binary 1/0 outputs) has been empirically shown to improve accuracy. This paper provides the first theoretical characterization of this phenomenon. In the setting of linearly separable binary classification with randomly corrupted labels, the authors prove that retraining can indeed improve the population accuracy compared to initial training with noisy labels. Retraining also has practical implications for local label differential privacy (DP), where models are trained with noisy labels. The authors propose consensus-based retraining, where retraining is done selectively on samples for which the predicted label matches the given noisy label. This approach significantly improves DP training accuracy at no additional privacy cost. For example, training ResNet-18 on CIFAR-100 with ε = 3 label DP achieves over 6% accuracy improvement with consensus-based retraining.