1.5k citations · 5.7k across the 60 of their papers we have counts for
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User Strategization and Trustworthy Algorithms
Sarah H. Cen, Andrew Ilyas, Aleksander Madry
Many human-facing algorithms -- including those that power recommender systems or hiring decision tools -- are trained on data provided by their users. The developers of these algo…
Rethinking Backdoor Attacks
Alaa Khaddaj, Guillaume Leclerc, Aleksandar Makelov +4
In a backdoor attack, an adversary inserts maliciously constructed backdoor examples into a training set to make the resulting model vulnerable to manipulation. Defending against s…
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…
A User-Driven Framework for Regulating and Auditing Social Media
Sarah H. Cen, Aleksander Madry, Devavrat Shah
People form judgments and make decisions based on the information that they observe. A growing portion of that information is not only provided, but carefully curated by social med…
TRAK: Attributing Model Behavior at Scale
Sung Min Park, Kristian Georgiev, Andrew Ilyas +2
The goal of data attribution is to trace model predictions back to training data. Despite a long line of work towards this goal, existing approaches to data attribution tend to for…
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…