19 citations · 25 across the 4 of their papers we have counts for
5 papers
From Protoscience to Epistemic Monoculture: How Benchmarking Set the Stage for the Deep Learning Revolution
Bernard J. Koch, David Peterson
Over the past decade, AI research has focused heavily on building ever-larger deep learning models. This approach has simultaneously unlocked incredible achievements in science and…
DMLR: Data-centric Machine Learning Research -- Past, Present and Future
Luis Oala, Manil Maskey, Lilith Bat-Leah +35
Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure developm…
Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research
Bernard Koch, Emily Denton, Alex Hanna +1
Benchmark datasets play a central role in the organization of machine learning research. They coordinate researchers around shared research problems and serve as a measure of progr…
A Primer on Deep Learning for Causal Inference
Bernard Koch, Tim Sainburg, Pablo Geraldo +3
This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction on ho…
Uncovering Sociological Effect Heterogeneity using Machine Learning
Jennie E. Brand, Jiahui Xu, Bernard Koch +1
Individuals do not respond uniformly to treatments, events, or interventions. Sociologists routinely partition samples into subgroups to explore how the effects of treatments vary…