14 citations · 28 across the 4 of their papers we have counts for
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stat.ML2022★ 1 cited
Ensembling over Classifiers: a Bias-Variance Perspective
Neha Gupta, Jamie Smith, Ben Adlam +1
Ensembles are a straightforward, remarkably effective method for improving the accuracy,calibration, and robustness of models on classification tasks; yet, the reasons that underli…
stat.ML2022★ 10 cited
Understanding the bias-variance tradeoff of Bregman divergences
Ben Adlam, Neha Gupta, Zelda Mariet +1
This paper builds upon the work of Pfau (2013), which generalized the bias variance tradeoff to any Bregman divergence loss function. Pfau (2013) showed that for Bregman divergence…