Moreover, distilling a synthetic version of sensitive data helps preserve privacy; a support set can beprovided toanend-user forthedownstream applications without disclosure ofdata.

Forlinear regression, we give a polynomial-time algorithm based on Celis-Dennis-Tapia optimization algorithms. For binary classification, we show how to efficiently implement itusing aproper agnostic learner (i.e., anEmpirical Risk Minimizer) for the class of interest.

Most,however,are nonparametric approaches and so can be difficult to scale.

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.