18 citations · 75 across the 21 of their papers we have counts for
6 papers · 1 filter
Large-Scale, Longitudinal Study of Large Language Models During the 2024 US Election Season
Sarah H. Cen, Andrew Ilyas, Hedi Driss +4
The 2024 US presidential election is the first major contest to occur in the US since the popularization of large language models (LLMs). Building on lessons from earlier shifts in…
Probably Approximately Correct Labels
Emmanuel J. Candès, Andrew Ilyas, Tijana Zrnic
Obtaining high-quality labeled datasets is often costly, requiring either human annotation or expensive experiments. In theory, powerful pre-trained AI models provide an opportunit…
DataMIL: Selecting Data for Robot Imitation Learning with Datamodels
Shivin Dass, Alaa Khaddaj, Logan Engstrom +3
Recently, the robotics community has amassed ever larger and more diverse datasets to train generalist policies. However, while these policies achieve strong mean performance acros…
AI Supply Chains: An Emerging Ecosystem of AI Actors, Products, and Services
Aspen Hopkins, Sarah H. Cen, Andrew Ilyas +3
The widespread adoption of AI in recent years has led to the emergence of AI supply chains: complex networks of AI actors contributing models, datasets, and more to the development…
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…
Optimizing ML Training with Metagradient Descent
Logan Engstrom, Andrew Ilyas, Benjamin Chen +3
A major challenge in training large-scale machine learning models is configuring the training process to maximize model performance, i.e., finding the best training setup from a va…