44 citations · 64 across the 4 of their papers we have counts for
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cs.LG2023★ 1 cited
Improving Representational Continuity via Continued Pretraining
Michael Sun, Ananya Kumar, Divyam Madaan +1
We consider the continual representation learning setting: sequentially pretrain a model on tasks , and then adapt on a small amount of data from each t…
cs.LG2022★ 7 cited
Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift
Ananya Kumar, Tengyu Ma, Percy Liang +1
We often see undesirable tradeoffs in robust machine learning where out-of-distribution (OOD) accuracy is at odds with in-distribution (ID) accuracy: a robust classifier obtained v…
cs.LG2021★ 12 cited
Extending the WILDS Benchmark for Unsupervised Adaptation
Shiori Sagawa, Pang Wei Koh, Tony Lee +17
Machine learning systems deployed in the wild are often trained on a source distribution but deployed on a different target distribution. Unlabeled data can be a powerful point of…