8 citations · 12 across the 6 of their papers we have counts for
5 papers · 1 filter
The Pragmatic Frames of Spurious Correlations in Machine Learning: Interpreting How and Why They Matter
Samuel J. Bell, Skyler Wang
Learning correlations from data forms the foundation of today's machine learning (ML) and artificial intelligence research. While contemporary methods enable the automatic discover…
An Effective Theory of Bias Amplification
Arjun Subramonian, Samuel J. Bell, Levent Sagun +1
Machine learning models can capture and amplify biases present in data, leading to disparate test performance across social groups. To better understand, evaluate, and mitigate the…
Reassessing the Validity of Spurious Correlations Benchmarks
Samuel J. Bell, Diane Bouchacourt, Levent Sagun
Neural networks can fail when the data contains spurious correlations. To understand this phenomenon, researchers have proposed numerous spurious correlations benchmarks upon which…
The Effect of Task Ordering in Continual Learning
Samuel J. Bell, Neil D. Lawrence
We investigate the effect of task ordering on continual learning performance. We conduct an extensive series of empirical experiments on synthetic and naturalistic datasets and sho…
Perspectives on Machine Learning from Psychology's Reproducibility Crisis
Samuel J. Bell, Onno P. Kampman
In the early 2010s, a crisis of reproducibility rocked the field of psychology. Following a period of reflection, the field has responded with radical reform of its scientific prac…