research
∙
08/02/2023
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models
We introduce OpenFlamingo, a family of autoregressive vision-language mo...
share
research
∙
02/23/2023
Out-of-Domain Robustness via Targeted Augmentations
Models trained on one set of domains often suffer performance drops on u...
research
∙
12/09/2021
Extending the WILDS Benchmark for Unsupervised Adaptation
Machine learning systems deployed in the wild are often trained on a sou...
research
∙
07/19/2021
Just Train Twice: Improving Group Robustness without Training Group Information
Standard training via empirical risk minimization (ERM) can produce mode...
research
∙
07/09/2021
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 pe...
research
∙
12/14/2020
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Distribution shifts can cause significant degradation in a broad range o...
research
∙
10/27/2020
Selective Classification Can Magnify Disparities Across Groups
Selective classification, in which models are allowed to abstain on unce...
research
∙
05/09/2020
An Investigation of Why Overparameterization Exacerbates Spurious Correlations
We study why overparameterization – increasing model size well beyond th...
research
∙
11/20/2019
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
Overparameterized neural networks can be highly accurate on average on a...
research
∙
10/21/2019
Multi-Resolution Weak Supervision for Sequential Data
Since manually labeling training data is slow and expensive, recent indu...
research
∙
09/04/2019