Abstract: We consider a class of distributed submodular maximization problems in which each agent must choose a single strategy from its strategy set. The global objective is to maximize a submodular ...

Abstract: We study the problem of incorporating risk while making combinatorial decisions under uncertainty. We formulate a discrete submodular maximization problem for selecting a set using ...

This is a reference implementation for "Object-Centric Learning with Slot Attention" (https://arxiv.org/abs/2006.15055) by Francesco Locatello, Dirk Weissenborn ...

Numerical experiments compare the three algorithms across three standard test problems (DR-submodular Quadratic Programming, Finite Sum of DR-submodular Quadratic Functions, Regular Coverage Function) ...

Under idealized conditions, an acquisition function may appear to improve accuracy per label. Under realistic conditions, the same method must also justify the effort it requires, the assumptions it ...