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Derandomizing Knockoffs
Zhimei Ren, Yuting Wei, Emmanuel Candès
Model-X knockoffs is a general procedure that can leverage any feature importance measure to produce a variable selection algorithm, which discovers true effects while rigorously c…
Achieving Equalized Odds by Resampling Sensitive Attributes
Yaniv Romano, Stephen Bates, Emmanuel J. Candès
We present a flexible framework for learning predictive models that approximately satisfy the equalized odds notion of fairness. This is achieved by introducing a general discrepan…
Classification with Valid and Adaptive Coverage
Yaniv Romano, Matteo Sesia, Emmanuel J. Candès
Conformal inference, cross-validation+, and the jackknife+ are hold-out methods that can be combined with virtually any machine learning algorithm to construct prediction sets with…
Knockoffs with Side Information
Zhimei Ren, Emmanuel Candès
We consider the problem of assessing the importance of multiple variables or factors from a dataset when side information is available. In principle, using side information can all…