Communication compression isacrucial technique formodern distributedlearning systems to alleviate their communication bottlenecks over slower networks.

While large language models have demonstrated this ability, how in-context learning could be performed over graphs is unexplored. In this paper, we develop Pr etraining O ver D iverse I n-Context G raph S y stems (PRODIGY), the first pretraining framework that enables in-context learning over graphs.

In this work, we propose a Bayesian formulation of deconditioning which naturally recovers the initial reproducing kernel Hilbert space formulation from Hsu and Ramos[1]. We extend deconditioning to a downscaling setup and devise efficient conditional mean embedding estimator for multiresolution data.

In human-AI collaboration systems for critical applications, in order to ensure minimal error, users should set an operating point based on model confidence to determine when the decision should be delegated to human experts.