Supervised Learning

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How many labelers do you have? A closer look at gold-standard labels
Jun 24, 2022Chen Cheng, Hilal Asi, John DuchiModel Calibration Noisy-Label Learning
Department of Statistics, Stanford University · Department of Electrical Engineering, Stanford University · Departments of Statistics and Electrical Engineering, Stanford University

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How many labelers do you have? A closer look at gold-standard labels

Chen Cheng, Hilal Asi, John Duchi

Department of Statistics, Stanford University · Department of Electrical Engineering, Stanford University · Departments of Statistics and Electrical Engineering, Stanford University

Model Calibration Noisy-Label Learning Dataset Aggregation Statistical Learning Theory Supervised Learning

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The construction of most supervised learning datasets revolves around collecting multiple labels for each instance, then aggregating the labels to form a type of "true" label. We question the wisdom of this pipeline by developing a (stylized) theoretical model of this process and analyzing its statistical consequences, showing how access to non-aggregated label information can make training well-calibrated models more feasible than it is with cleaned labels. The entire story, however, is subtle, and the contrasts between aggregated and fuller label information depend on the particulars of the problem, where estimators that use aggregated information exhibit robust but slower rates of convergence, while estimators that can effectively leverage all labels converge more quickly if they have fidelity to (or can learn) the true labeling process. The theory makes several predictions for real-world datasets, including when non-aggregate labels should improve learning performance, which we test to corroborate the validity of our predictions.