139 citations · 343 across the 11 of their papers we have counts for
3 papers · 1 filter
Small-to-Large Generalization: Data Influences Models Consistently Across Scale
Alaa Khaddaj, Logan Engstrom, Aleksander Madry
Choice of training data distribution greatly influences model behavior. Yet, in large-scale settings, precisely characterizing how changes in training data affects predictions is o…
MAGIC: Near-Optimal Data Attribution for Deep Learning
Andrew Ilyas, Logan Engstrom
The goal of predictive data attribution is to estimate how adding or removing a given set of training datapoints will affect model predictions. In convex settings, this goal is str…
Optimizing ML Training with Metagradient Descent
Logan Engstrom, Andrew Ilyas, Benjamin Chen +3
A major challenge in training large-scale machine learning models is configuring the training process to maximize model performance, i.e., finding the best training setup from a va…