Statistical Learning
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox
Inspired by a recent breakthrough of Mishchenko et al. [2022], who for the first
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox
Inspired by a recent breakthrough of Mishchenko et al. [2022], who for the first
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
We formulate a joint probabilistic model that considers a prior distribution over graphs along with a GCN-based likelihood and develop a stochastic variational inference algorithm to estimate the graph posterior and the GCN parameters jointly.