Fairness in clustering has been considered extensively in the past; however, the trade-off between the two objectives -- e.g., can we sacrifice just a little in the quality of the clustering to significantly increase fairness, or vice-versa?

Probabilistic predictions are at the heart of modern data science.

Gittins index, which can be reinterpreted as an acquisition function.

We study the convergence behavior of a generalized Frank-Wolfe algorithm in constrained (stochastic) monotone variational inequality (MVI) problems. In recent years, there have been numerous efforts to design algorithms for solving constrained MVI problems due to their connections with optimization, machine learning, and equilibrium computation in games.

Current approaches to mitigate these representational harms balance the number of retrieved items across population groups defined by a small number of (often binary) attributes. However, most existing methods overlook intersectional groups determined by combinations of group attributes, such as gender, race, and ethnicity.

These features are then fed into a V alue Classification Network (VCN) which directly generates classification solutions.

In the basic recommendation paradigm, the most (predicted) relevant item is recommended to each user.

Image Signal Processors (ISPs) play a fundamental role in camera systems.