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Found 5 papers, 1 papers with code
Conformal Counterfactual Inference under Hidden Confounding
Personalized decision making requires the knowledge of potential outcomes under different treatments, and confidence intervals about the potential outcomes further enrich this decision-making process and improve its reliability in high-stakes scenarios.
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.
Inference-time Stochastic Ranking with Risk Control
Widely used deterministic LTR models can lead to unfair exposure distribution, especially when items with the same relevance receive slightly different ranking scores.
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.
