Classical causal structure learning algorithms often assume no latent confounders. However, it is usually impossible to enumerate and measure all task-related variables in real-world scenarios. Neglecting latent confounders may lead to spurious correlations among observed variables.

We propose a novel class of graph neural networks based on the discretised Beltrami flow, a non-Euclidean diffusion PDE.

In this paper, we focus on a specific federated learning scenario: Vertical Federated Learning(VFL) [5].

However, for the recommender system experiment, there are no natural representations for the candidate models. IS-g/DR-g Off-policy evaluation (OPE) methods can provide an estimate of the accumulative metric. The resulting methods aredenoted asIS-EI andDR-EIrespectively. Asthere arelimited information tobegained byrepeatedly deploying thesame model online, we exclude the models that have been deployed when choosing the next model to deploy for all the methodsincludingAOE. We simulate the "online" deployment scenario as follows: a multi-class classifier is given a set of inputs; for each input, the classifier returns a prediction of the label and only a binary immediate feedback about whether the predicted class is correct is available. They-axisshowsthe gap in the accumulativemetric between the optimal model and the estimated best model by each method.