18 citations · 75 across the 21 of their papers we have counts for
9 papers · 1 filter
Pick Your Poison: Learning to Select Poison Sets for Stronger LLM Backdoor Attacks
Aashiq Muhamed, Mona T. Diab, Virginia Smith +2
Backdoor poisoning attacks add poisoned examples to otherwise-clean finetuning data, pairing a trigger with a target behavior that the model learns to produce when the trigger appe…
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…
Attribute-to-Delete: Machine Unlearning via Datamodel Matching
Kristian Georgiev, Roy Rinberg, Sung Min Park +4
Machine unlearning -- efficiently removing the effect of a small "forget set" of training data on a pre-trained machine learning model -- has recently attracted significant researc…
Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selection
Saachi Jain, Kimia Hamidieh, Kristian Georgiev +3
Machine learning models can fail on subgroups that are underrepresented during training. While techniques such as dataset balancing can improve performance on underperforming group…
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…
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…
ModelDiff: A Framework for Comparing Learning Algorithms
Harshay Shah, Sung Min Park, Andrew Ilyas +1
We study the problem of (learning) algorithm comparison, where the goal is to find differences between models trained with two different learning algorithms. We begin by formalizin…
When does Bias Transfer in Transfer Learning?
Hadi Salman, Saachi Jain, Andrew Ilyas +3
Using transfer learning to adapt a pre-trained "source model" to a downstream "target task" can dramatically increase performance with seemingly no downside. In this work, we demon…