LargeNumberofNodes In this section, we test DAGMA, GOLEM, and NOTEARS for graphs with number of variables d {200,300,500,800,1000}.

However, it is important to consider users' privacy in the dataset, since results from data

Wefixp tobeq to toexaminep is constructed function functionR(i) = si i).

The Gumbel-Max trick is the basis of many relaxed gradient estimators. These estimators areeasy toimplement and lowvariance, butthegoal ofscaling them comprehensively to large combinatorial distributions is still outstanding. Working within the perturbation model framework, we introduce stochastic softmax tricks, which generalizetheGumbel-Softmax tricktocombinatorial spaces.

However, it is important to consider users' privacy in the dataset, since results from data

Many problems in machine learning reduce to learning a probability distribution (or policy) over sequences of discrete actions so as to maximize a downstream utility function. Examples include generating text sequences to maximize a task-specific metric like BLEU and generating action sequences in reinforcement learning (RL) to maximize expected return.