Skip to yearly menu bar Skip to main content

Spotlight Poster

On Bias-Variance Alignment in Deep Models

Lin Chen · Michal Lukasik · Wittawat Jitkrittum · Chong You · Sanjiv Kumar

Halle B #155
[ ]
Tue 7 May 1:45 a.m. PDT — 3:45 a.m. PDT


Classical wisdom in machine learning holds that the generalization error can be decomposed into bias and variance, and these two terms exhibit a \emph{trade-off}. However, in this paper, we show that for an ensemble of deep learning based classification models, bias and variance are \emph{aligned} at a sample level, where squared bias is approximately \emph{equal} to variance for correctly classified sample points. We present empirical evidence confirming this phenomenon in a variety of deep learning models and datasets. Moreover, we study this phenomenon from two theoretical perspectives: calibration and neural collapse. We first show theoretically that under the assumption that the models are well calibrated, we can observe the bias-variance alignment. Second, starting from the picture provided by the neural collapse theory, we show an approximate correlation between bias and variance.

Chat is not available.