SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Hyperparameter	Search range
Num. of covariate layers	{2,4,6}
Num. of treatment layers	{2,4,6}
Num. of layers	{2,4,6}
Dim. of hidden covariate layers	{128,256}
Independence regularization coefficient	{0,0.01,0.1,1.0}
Dropout	{0,0.1}
Model dim.	{512,1024}
Learning rate	{3×10-3,1×10-3,3×10-4,1×10-4}
Meta learning rate	{0.1,0.5,0.9}
Weight decay	{0.1}
Reptile k	{1,2,3}
L1 regularization coefficient	{0,1×10-7,5×10-7}
Table 18: Hyperparameter search space for the GraphITE baseline on the LINCS dataset.
Hyperparameter	Search range
Dropout	[1e-4,1e-1]
Learning rate	[1e-5,1e-3]
Weight decay	[1e-5,1e-2]
Adversarial temperature	[1,10]
Gamma	[0,30]
Num. of sampled neighbors	0-10
Dim. of hidden layers	{64, 128, 256, 512}
Table 19: The hyperparameter optimization search ranges used in the selection of the optimal model for the generation of knowledge graph node embeddings that would serve as intervention information for the medical claims dataset.