MPNNs have been studied extensively in theory and practice [Böker et al., 2023, Gilmer et al., 2017,

We propose a new exact minibatch MH method, TunaMH, which exposes a tunable trade-off between its batch size and its theoretically guaranteed convergence rate.

An example is the graph convolutional kernel support vector machine (GCKSVM) for node classification, for which we analyze the generalization error bound and discuss the impact of the graph structure.

In this work, we empirically investigate the impact of neural parameter symmetries by introducing new neural network architectures that have reduced parameter space symmetries.

Federated learning (FL) "involves training statistical models over remote devices or siloed data centers,suchasmobile phones orhospitals, whilekeepingdatalocalized"[56]because ofprivacy concerns orlimitedcommunication resources. Hence, clients only communicate with apotentially far-away (e.g., in another continent) orchestrator and do not Recent experimental and theoretical work suggests that, in practice,the first effect has been over-estimated by classic worst-caseconvergencebounds.

Weassume that our tree divides the bounded reward range[rmin,rmax] uniformly into2d bins at each leveld D. By labelling left branches ofanode by0,and right branches with a1,we can associate aunique binary stringb1:d to any single internal (d < D) or leaf (d = D) node in the tree. Thedth element, when it exists, is denoted asbd. The root node is denoted by empty string . We should note that even though this exponential term might initially seem discouraging, we setD = 3in our experiments and observe no significant improvements for largerD. Algorithm 1 CTREE, performs regression utilizing a tree-based discetization, where nodes are composedofGLNs.