The various clustering problems that have been studied intensively include, e.g., the

While some adaptive experimental strategies exist for the identification of the single best subgroup, they commonly do not enable the identification of the best set of subgroups.

In online learning literature, stochastic and adversarial settings are two of the most well-studied cases.

Document retrieval plays an essential role in web search applications and various downstream knowledge-intensive tasks by identifying relevant documents to satisfy users' queries.

The remarkable attention which fair clustering has received in the last few years has resulted in a significant number of different notions of fairness.

By noticing that APPR is a local variant of Gauss-Seidel, this paper explores the question of whether standard iterative solvers can be effectively localized . We propose to use the locally evolving set process, a novel framework to characterize the algorithm locality, and demonstrate that many standard solvers can be effectively localized.

While current KGE methods have shown success, many are limited to the graph structure alone, neglecting the wealth of open-world knowledge surrounding entities not explicitly depicted in the KG, which is manually created in most cases.

Moreover, how to further enhance view cooperation for better fusion still needs to be explored.