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We introduce an adaptive method with formal quality guarantees for weaksupervision in a non-stationary setting. Our goal is to infer the unknownlabels of a sequence of data by using weak supervision sources that provideindependent noisy signals of the correct classification for each data point.This setting includes crowdsourcing and programmatic weak supervision. We focuson the non-stationary case, where the accuracy of the weak supervision sourcescan drift over time, e.g., because of changes in the underlying datadistribution. Due to the drift, older data could provide misleading informationto infer the label of the current data point. Previous work relied on a prioriassumptions on the magnitude of the drift to decide how much data to use fromthe past. Comparatively, our algorithm does not require any assumptions on thedrift, and it adapts based on the input. In particular, at each step, ouralgorithm guarantees an estimation of the current accuracies of the weaksupervision sources over a window of past observations that minimizes atrade-off between the error due to the variance of the estimation and the errordue to the drift. Experiments on synthetic and real-world labelers show thatour approach indeed adapts to the drift. Unlike fixed-window-size strategies,it dynamically chooses a window size that allows it to consistently maintaingood performance.

An Adaptive Method for Weak Supervision with Drifting Data