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stat.ML2023
Counterfactually Comparing Abstaining Classifiers
Yo Joong Choe, Aditya Gangrade, Aaditya Ramdas
Abstaining classifiers have the option to abstain from making predictions on inputs that they are unsure about. These classifiers are becoming increasingly popular in high-stakes d…
stat.ML2020
An Empirical Study of Invariant Risk Minimization
Yo Joong Choe, Jiyeon Ham, Kyubyong Park
Invariant risk minimization (IRM) (Arjovsky et al., 2019) is a recently proposed framework designed for learning predictors that are invariant to spurious correlations across diffe…