A Uni(cid:12)ed Bias-Variance Decomposition

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Scaling description of generalization with number of parameters in deep learning (2019, methodology): “5 In spirit, this section shares some similarity with the bias variance decomposition developed in [45], except that we consider averaging on initial conditions instead of training set, and that we use the average output function as predictor, rather than applying the majority rule on a set of predi”
Learning from Noisy Labels with Distillation (2017, methodology): “For the squared loss s̄(x)sq = averageDt(fDc(x)), and for the 0-1 loss s̄(x)0-1 = medianDt(fDc(x)) [7].”
Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] (2016, methodology): “For multiple datasets, bias-variancecovariance decomposition is introduced [12]–[15] and the equation is shown:”
Model Multiplicity: Opportunities, Concerns, and Solutions (2022, background): “Conceptually, errors in machine learning systems come from three sources: bias, variance, and irreducible noise [25, 33].”
An Information-Theoretic View for Deep Learning (2018, background): “Usually, if a model ts the training data too well, it may generalize poorly on the test data; this is known as the bias-variance trade-o problem (Domingos 2000).”