Despite achieving remarkable performance, deep graph learning models, such as node classification and network embedding, suffer from harassment caused by small adversarial perturbations.

Let us present the complete theorem first, and then give its proof. We are now ready to present Theorem 1. Theorem 1 But since F induces a different DAG, F is not identified up to trivialities. Proposition 4. F or any graph G there exists F F There are two cases to consider. The backward direction is obvious. This follows from definitions and acyclicity.1.4.5 Proof of Theorem 3 Theorem 3. Then F is identifiable up to trivialities.

In this paper, we aim at bridging this gap and derive various theoretical properties of sliced probability divergences.

We specialize to the 2-player 0-sum case in presenting prior work, since some papers in the literature only consider this setting.

We specialize to the 2-player 0-sum case in presenting prior work, since some papers in the literature only consider this setting.

For very large models this requirement can be prohibitive.

Moran (NeurIPS 2019) showed that uniform or highly sample-efficient pure-private learners can be time-efficiently compiled into online learners.

A transcompiler, transpiler, or source-to-source compiler, is a translator which converts between programming languages that operate at a similar level of abstraction.

(line 4). We show our RG-Explainer for graph classification in Alg. 2. The algorithm is similar to the one explaining node classifications, except that we train our seed locator to detect the most influential (line 4). Input: The input graph G = ( V, E), node features X, node instances I, and a trained GNN model f () . Check the stopping criteria by Eq. 10. I, and a trained GNN model f () .