Graph contrastivelearning attracts/disperses node representations forsimilar/dissimilar node pairs under some notion of similarity.

What sets this setting apart from traditional stochastic optimization is the difference between merely updating model parameters and deploying the new model.

Compared with the existing optimization analysis for Meta-Learning in FL, federated conditional stochastic optimization considers the sample of tasks.

Compared with the existing optimization analysis for Meta-Learning in FL, federated conditional stochastic optimization considers the sample of tasks.

Density-ratio estimation via classification is a cornerstone of unsupervised learning.

'Sensitivity' indicates the distance metric imposed onD(θ) when the latter is subject to perturbation, given in the formd(D(θ),D(θ)) ϵ θ θ such that d(,) is a distance metric between distributions.