How Can We Harness Pre-Training to Develop Robust Models?
We explore a simple principle for harnessing pre-training to develop robust models.Ask Your Distribution Shift if Pre-Training is Right for You
We study the robustness benefits of pre-training and characterize failure modes that pre-training can and cannot address.DsDm: Model-Aware Dataset Selection with Datamodels
Selecting better data by approximating how models learn from data.How Training Data Guides Diffusion Models
We introduce a new framework for data attribution in generative settings, and propose an efficient method to attribute diffusion models.Rethinking Backdoor Attacks
We introduce a new perspective on backdoor attacks and defenses in deep learning.TRAK-ing Model Behavior with Data
We introduce TRAK, a new data attribution method that scales to large(r) models!Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual Generation
We introduce dataset interfaces, a scalable framework that synthesizes counterfactual examples under user-specified shiftsTailored Data Augmentation to Mitigate Model Failures
We demonstrate how we can use Stable Diffusion to target a model's failure modes
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