24 citations · 53 across the 7 of their papers we have counts for
3 papers
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.LG2022★ 24 cited
Test-Time Adaptation via Conjugate Pseudo-labels
Sachin Goyal, Mingjie Sun, Aditi Raghunathan +1
Test-time adaptation (TTA) refers to adapting neural networks to distribution shifts, with access to only the unlabeled test samples from the new domain at test-time. Prior TTA met…
stat.ML2016★ 6 cited
Estimation from Indirect Supervision with Linear Moments
Aditi Raghunathan, Roy Frostig, John Duchi +1
In structured prediction problems where we have indirect supervision of the output, maximum marginal likelihood faces two computational obstacles: non-convexity of the objective an…