We study the proportional clustering problem of Chen et al. (ICML '19) and relate

In this section, we report the hyperparameters of each base model used in our paper, details in Table 2. The only hyperparameter that is tuned is done per dataset using a 10% validation split. In this Section, we discuss the experimental convergence of our U-DIF algorithm to the global optimum. In order to approximately compute the true global optimum, we use the following numerical scheme. (exact numbers vary by network and are given in Figure 4).

To combat this, there has been a surge of interest in algorithmic fairness metrics (Mehrabi et al., 2021).

Unlikepriorworkon individual fairness, we do not assume the similarity measure among individuals is known, nor do we assume that such measure takes a certain parametric form.

Fair representation learning provides an effective way of enforcing fairness constraints without compromising utility for downstream users.