37 citations · 62 across the 10 of their papers we have counts for
10 papers
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…
Towards Geographic Inclusion in the Evaluation of Text-to-Image Models
Melissa Hall, Samuel J. Bell, Candace Ross +3
Rapid progress in text-to-image generative models coupled with their deployment for visual content creation has magnified the importance of thoroughly evaluating their performance…
Simplicity Bias Leads to Amplified Performance Disparities
Samuel J. Bell, Levent Sagun
Which parts of a dataset will a given model find difficult? Recent work has shown that SGD-trained models have a bias towards simplicity, leading them to prioritize learning a majo…
Modeling the Machine Learning Multiverse
Samuel J. Bell, Onno P. Kampman, Jesse Dodge +1
Amid mounting concern about the reliability and credibility of machine learning research, we present a principled framework for making robust and generalizable claims: the multiver…
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…
Behavioral Experiments for Understanding Catastrophic Forgetting
Samuel J. Bell, Neil D. Lawrence
In this paper we explore whether the fundamental tool of experimental psychology, the behavioral experiment, has the power to generate insight not only into humans and animals, but…
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…
Context is Key: Grammatical Error Detection with Contextual Word Representations
Samuel Bell, Helen Yannakoudakis, Marek Rei
Grammatical error detection (GED) in non-native writing requires systems to identify a wide range of errors in text written by language learners. Error detection as a purely superv…