100 citations · 169 across the 11 of their papers we have counts for
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stat.ML2020
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…
stat.ML2020★ 1 cited
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…