430 citations · 643 across the 26 of their papers we have counts for
3 papers · 2 filters
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…
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions
Courtney Paquette, Elliot Paquette, Ben Adlam +1
Stochastic gradient descent (SGD) is a pillar of modern machine learning, serving as the go-to optimization algorithm for a diverse array of problems. While the empirical success o…
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…