5 citations · 5 across the 2 of their papers we have counts for
3 papers
cs.LG2024
Evaluating Model Performance Under Worst-case Subpopulations
Mike Li, Daksh Mittal, Hongseok Namkoong +1
The performance of ML models degrades when the training population is different from that seen under operation. Towards assessing distributional robustness, we study the worst-case…
cs.CV2021★ 5 cited
Robust fine-tuning of zero-shot models
Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim +8
Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a sp…
stat.ML2021
Linear Classifiers Under Infinite Imbalance
Paul Glasserman, Mike Li
We study the behavior of linear discriminant functions for binary classification in the infinite-imbalance limit, where the sample size of one class grows without bound while the s…