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Found 4 papers, 1 papers with code
Fair Classifiers that Abstain without Harm
To generalize the abstaining decisions to test samples, we then train a surrogate model to learn the abstaining decisions based on the IP solutions in an end-to-end manner.
Fair Learning to Rank with Distribution-free Risk Control
Learning to Rank (LTR) methods are vital in online economies, affecting users and item providers.
Grassmann Stein Variational Gradient Descent
Stein variational gradient descent (SVGD) is a deterministic particle inference algorithm that provides an efficient alternative to Markov chain Monte Carlo.
BayesIMP: Uncertainty Quantification for Causal Data Fusion
While causal models are becoming one of the mainstays of machine learning, the problem of uncertainty quantification in causal inference remains challenging.
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