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cs.LG2022★ 1 cited
Sufficient Invariant Learning for Distribution Shift
Taero Kim, Subeen Park, Sungjun Lim +3
Learning robust models under distribution shifts between training and test datasets is a fundamental challenge in machine learning. While learning invariant features across environ…
cs.LG2022
Learning Fair Representation via Distributional Contrastive Disentanglement
Changdae Oh, Heeji Won, Junhyuk So +4
Learning fair representation is crucial for achieving fairness or debiasing sensitive information. Most existing works rely on adversarial representation learning to inject some in…