18 citations · 75 across the 21 of their papers we have counts for
4 papers · 1 filter
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…
Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content
Sarah H. Cen, Andrew Ilyas, Jennifer Allen +2
Most modern recommendation algorithms are data-driven: they generate personalized recommendations by observing users' past behaviors. A common assumption in recommendation is that…
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…