The group fairness perspective imposes constraints like demographic parity (Calders et al., 2009; Zliobaite, 2015) and equalized odds (Hardt et al., 2016). Although similar in spirit, our work sidesteps this need to define a similarity metric between agents in the feature space. Rather, we view an agent's Ranking has been widely studied in the field of Information Retrieval (IR), mostly in the context of optimizing user utility. The Probability Ranking Principle (PRP) (Robertson, 1977), a guiding principle for ranking in IR, states that user utility is optimal when documents (i.e., the agents) are Besides ranking diversity, IR methods have dealt with uncertainty in relevance that comes via users' implicit or explicit feedback (Penha and Hauff, 2021; Soufiani et al., 2012), as well as stochasticity arising Kearns et al. (2017) present a way to fairly select Hence, they propose using the true CDF rank as a derived merit criterion that can be compared. Thus, a fair principal stands to gain more by obtaining perfect information.

Predictive distributions quantify uncertainties ignored by point estimates.

Currently, the data augmentation parameters are chosen by human effort and costly cross-validation, which makes it cumbersome to apply to new datasets. We develop a convenient gradient-based method for selecting the data augmentation without validation data during training of a deep neural network.

Because the true underlying structure of the data is rarely accessible, this "representation learning" must be

When the forward model is unknown, or imperfect, artifacts due to model mismatch occur in the recovery of the source.

For the GP, the bias-variance trade-off is made by optimization of the two hyperparameters: the length scale and noise-term.