1.5k citations · 5.7k across the 60 of their papers we have counts for
26 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…
Decomposing and Editing Predictions by Modeling Model Computation
Harshay Shah, Andrew Ilyas, Aleksander Madry
How does the internal computation of a machine learning model transform inputs into predictions? In this paper, we introduce a task called component modeling that aims to address t…
DsDm: Model-Aware Dataset Selection with Datamodels
Logan Engstrom, Axel Feldmann, Aleksander Madry
When selecting data for training large-scale models, standard practice is to filter for examples that match human notions of data quality. Such filtering yields qualitatively clean…
FFCV: Accelerating Training by Removing Data Bottlenecks
Guillaume Leclerc, Andrew Ilyas, Logan Engstrom +3
We present FFCV, a library for easy and fast machine learning model training. FFCV speeds up model training by eliminating (often subtle) data bottlenecks from the training process…
Raising the Cost of Malicious AI-Powered Image Editing
Hadi Salman, Alaa Khaddaj, Guillaume Leclerc +2
We present an approach to mitigating the risks of malicious image editing posed by large diffusion models. The key idea is to immunize images so as to make them resistant to manipu…
Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual Generation
Joshua Vendrow, Saachi Jain, Logan Engstrom +1
Distribution shift is a major source of failure for machine learning models. However, evaluating model reliability under distribution shift can be challenging, especially since it…