40 citations · 112 across the 9 of their papers we have counts for
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cs.LG2021
Rethinking Importance Weighting for Transfer Learning
Nan Lu, Tianyi Zhang, Tongtong Fang +2
A key assumption in supervised learning is that training and test data follow the same probability distribution. However, this fundamental assumption is not always satisfied in pra…
cs.LG2021
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation
Takeshi Teshima, Masashi Sugiyama
Causal graphs (CGs) are compact representations of the knowledge of the data generating processes behind the data distributions. When a CG is available, e.g., from the domain knowl…