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Found 13 papers, 11 papers with code
Overparameterization hurts worst-group accuracy with spurious correlations
Increasing model capacity well beyond the point of zero training error has been observed to improve average test accuracy.
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models
We introduce OpenFlamingo, a family of autoregressive vision-language models ranging from 3B to 9B parameters.
Ranked #14 on
Visual Question Answering (VQA)
on InfiMM-Eval
Out-of-Domain Robustness via Targeted Augmentations
Models trained on one set of domains often suffer performance drops on unseen domains, e. g., when wildlife monitoring models are deployed in new camera locations.
Extending the WILDS Benchmark for Unsupervised Adaptation
Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from distributions beyond the source distribution as well.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Just Train Twice: Improving Group Robustness without Training Group Information
Standard training via empirical risk minimization (ERM) can produce models that achieve high accuracy on average but low accuracy on certain groups, especially in the presence of spurious correlations between the input and label.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
For machine learning systems to be reliable, we must understand their performance in unseen, out-of-distribution environments.
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild.
Selective Classification Can Magnify Disparities Across Groups
In this paper, we find that while selective classification can improve average accuracies, it can simultaneously magnify existing accuracy disparities between various groups within a population, especially in the presence of spurious correlations.
An Investigation of Why Overparameterization Exacerbates Spurious Correlations
We study why overparameterization -- increasing model size well beyond the point of zero training error -- can hurt test error on minority groups despite improving average test error when there are spurious correlations in the data.
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
Distributionally robust optimization (DRO) allows us to learn models that instead minimize the worst-case training loss over a set of pre-defined groups.
Ranked #1 on
Out-of-Distribution Generalization
on UrbanCars
Multi-Resolution Weak Supervision for Sequential Data
Multi-resolution sources exacerbate this challenge due to complex correlations and sample complexity that scales in the length of the sequence.
Distributionally Robust Language Modeling
Language models are generally trained on data spanning a wide range of topics (e. g., news, reviews, fiction), but they might be applied to an a priori unknown target distribution (e. g., restaurant reviews).
