3 citations · 9 across the 14 of their papers we have counts for
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stat.ML2025
Principled Out-of-Distribution Generalization via Simplicity
Jiawei Ge, Amanda Wang, Shange Tang +1
Modern foundation models exhibit remarkable out-of-distribution (OOD) generalization, solving tasks far beyond the support of their training data. However, the theoretical principl…
stat.ML2023★ 1 cited
Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift
Jiawei Ge, Shange Tang, Jianqing Fan +2
A key challenge of modern machine learning systems is to achieve Out-of-Distribution (OOD) generalization -- generalizing to target data whose distribution differs from that of sou…
stat.ML2023★ 3 cited
On the Provable Advantage of Unsupervised Pretraining
Jiawei Ge, Shange Tang, Jianqing Fan +1
Unsupervised pretraining, which learns a useful representation using a large amount of unlabeled data to facilitate the learning of downstream tasks, is a critical component of mod…