Our ideas also can be applied to the more general setting that the objective function only satisfies PL condition for one variable.

Unobserved discrete data are ubiquitous in many scientific disciplines, and how to learn the causal structure of these latent variables is crucial for uncovering data patterns. Most studies focus on the linear latent variable model or impose strict constraints on latent structures, which fail to address cases in discrete data involving non-linear relationships or complex latent structures.

Research efforts on learning implicit 3D shapes without 3D supervision have primarily resorted to binary occupancy[26,34]asthe representation, aiming tomatch reprojected 3D occupancytothe given binary masks. Current worksadopting signed distance functions (SDF) either require apretrained deep shape prior [27] or are limited to discretized representations [14] that do not scale up with resolution.

MAG-GNN effectively reduces the running time of subgraph GNNs.

'It was like a phone call suddenly connecting' Ai Weiwei. 'It was like a phone call suddenly connecting' Ai Weiwei. 'Was I scared going back to China? He has been jailed, tracked and threatened by China's government. What was it like pay a visit home?

Asinterventions areexpensive(require carefully controlled experiments) andperforming multiple interventions is time-consuming, an important goal in causal discovery is to design algorithms that utilize simple (preferably, single variable) and fewer interventions [Shanmugam et al.,2015]. However, when there are latents or unobserved variables in the system, in the worst-case, it is not possible to learn the exact causal DAG without intervening on every variable at least once.