133 citations · 152 across the 7 of their papers we have counts for
11 papers · 1 filter
Transformers Don't In-Context Learn Least Squares Regression
Joshua Hill, Benjamin Eyre, Elliot Creager
In-context learning (ICL) has emerged as a powerful capability of large pretrained transformers, enabling them to solve new tasks implicit in example input-output pairs without any…
Online Algorithmic Recourse by Collective Action
Elliot Creager, Richard Zemel
Research on algorithmic recourse typically considers how an individual can reasonably change an unfavorable automated decision when interacting with a fixed decision-making system.…
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Benjamin Eyre, Elliot Creager, David Madras +2
Designing deep neural network classifiers that perform robustly on distributions differing from the available training data is an active area of machine learning research. However,…
SURFSUP: Learning Fluid Simulation for Novel Surfaces
Arjun Mani, Ishaan Preetam Chandratreya, Elliot Creager +2
Modeling the mechanics of fluid in complex scenes is vital to applications in design, graphics, and robotics. Learning-based methods provide fast and differentiable fluid simulator…
Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification
Robert Adragna, Elliot Creager, David Madras +1
Robustness is of central importance in machine learning and has given rise to the fields of domain generalization and invariant learning, which are concerned with improving perform…
Environment Inference for Invariant Learning
Elliot Creager, Jörn-Henrik Jacobsen, Richard Zemel
Learning models that gracefully handle distribution shifts is central to research on domain generalization, robust optimization, and fairness. A promising formulation is domain-inv…