10 citations · 15 across the 11 of their papers we have counts for
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cs.LG2023
Invariant-Feature Subspace Recovery: A New Class of Provable Domain Generalization Algorithms
Haoxiang Wang, Gargi Balasubramaniam, Haozhe Si +2
Domain generalization asks for models trained over a set of training environments to generalize well in unseen test environments. Recently, a series of algorithms such as Invariant…
cs.LG2023★ 1 cited
Gradual Domain Adaptation: Theory and Algorithms
Yifei He, Haoxiang Wang, Bo Li +1
Unsupervised domain adaptation (UDA) adapts a model from a labeled source domain to an unlabeled target domain in a one-off way. Though widely applied, UDA faces a great challenge…
cs.LG2023★ 10 cited
Mitigating the Alignment Tax of RLHF
Yong Lin, Hangyu Lin, Wei Xiong +14
LLMs acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, w…