We present a novel framework, called GRAB (GRaphical models with overlApping Blocks), to capture densely connected components in a network estimate. GRAB takes as input a data matrix of p variables and n samples, and jointly learns both a network among p variables and densely connected groups of variables (called `blocks'). GRAB has four major novelties as compared to existing network estimation methods: 1) It does not require the blocks to be given a priori.

These features are then fed into a V alue Classification Network (VCN) which directly generates classification solutions.

License: Werelease the code used to build our benchmark and perform our experiments under theMITLicense (https://mit-license.org/),whereas werelease datawecreated, including the performance metrics collected by us, the splits used to train, validate and test our surrogate models, and our surrogate models, under the CCBY 4.0 License (https://creativecommons. Compute resources We trained the configurations on a large SLURM-based cluster with approximately 300,000 CPU-cores available in parallel. This ensures that all three data splits retain all or most of the statistical properties, including any biases, of the original performancedataset. Whereas fitting XGBoost used mean-squared-error as a regression metric, quality of fit for hyperparameters was judged using Kendall's tau rank correlation values. Task SpeedupoverHPO-only SpeedupoverNAS-only CIFAR-10 54.7 33.7 Colorectal-Histology 75.2 20.1 Fashion-MNIST 8.5 34.6 Geometricmean 32.7 28.6 resource consumption for our experiments performed on Intel(R) Xeon(R) Gold 6242 CPU @ 2.80GHztobe1.75CPU-core-hours.

Following the convention of RS, existing methods exploit unique user representation in their model design.

Following the convention of RS, existing methods exploit unique user representation in their model design.