12 citations · 13 across the 4 of their papers we have counts for
4 papers
Accelerated Dynamic Importance Weighting with Versatile Divergence-Minimizing Estimators
Tongtong Fang, Nan Lu, Gang Niu +2
Importance weighting (IW) is a golden solver for joint distribution shift, where the joint distributions differ between the training and test data. To solve this problem, IW estima…
Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems
Tongtong Fang, Nan Lu, Gang Niu +1
Distribution shift (DS) may have two levels: the distribution itself changes, and the support (i.e., the set where the probability density is non-zero) also changes. When consideri…
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…
Rethinking Importance Weighting for Deep Learning under Distribution Shift
Tongtong Fang, Nan Lu, Gang Niu +1
Under distribution shift (DS) where the training data distribution differs from the test one, a powerful technique is importance weighting (IW) which handles DS in two separate ste…