100 citations · 192 across the 13 of their papers we have counts for
3 papers · 1 filter
Efficient computation and analysis of distributional Shapley values
Yongchan Kwon, Manuel A. Rivas, James Zou
Distributional data Shapley value (DShapley) has recently been proposed as a principled framework to quantify the contribution of individual datum in machine learning. DShapley dev…
Principled learning method for Wasserstein distributionally robust optimization with local perturbations
Yongchan Kwon, Wonyoung Kim, Joong-Ho Won +1
Wasserstein distributionally robust optimization (WDRO) attempts to learn a model that minimizes the local worst-case risk in the vicinity of the empirical data distribution define…
Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric
Yongchan Kwon, Wonyoung Kim, Masashi Sugiyama +1
We consider the problem of learning a binary classifier from only positive and unlabeled observations (called PU learning). Recent studies in PU learning have shown superior perfor…