Work done during internship at Google. Work done during visiting-faculty appointment at Google.

The mainstream paradigm for UDA is feature adaptation, a.k.a.

RDP inherits and generalizes the information-theoretic properties of DP . This composition rule, together with Lemma 3, often allows for tighter calculations of (null,δ)-DP for the composed mechanism than directly invoking the strong composition theorem below. Also w.l.o.g., we assume thresholds Substituting the above expression to the definition of RDP and apply Jensen's inequality (6) = The inequality applies Jensen's inequality to bivariate function We use a trick due to [Bun and Steinke, 2016] with some modifications. Now we are ready to prove the three claims of Theorem 8. 13 The claim (3): Substitute the the above bound into Lemma 17, we get: E In the last line, we applied the "indistinguishability" property of an RDP mechanism in Lemma 15 The issue is how to proceed. The proof follows a similar sequence of arguments to that we presented for c = 1 .

How can this training-evaluation gap be addressed? While specific techniques have been adopted to optimize certain confusion matrix based metrics, it is challenging or impossible in some cases to generalize the techniques to other metrics.

We appreciate the valuable feedback from all the reviewers and will include the following discussion into our work. We believe that this is not an elegant way to have a global view of the GNN model. "Since the explanatory motifs are not learned end-to-end, the model however may suffer from sub-optimal generalization PGExplainer is natively designed for collectively explaining multiple instances. The source code of PGExplainer can be found in GitHub with the name "PGExplainer". We follow the experimental setting in GNNExplainer. "explanation accuracy" is not formally defined. We didn't report std for baselines because they don't have sampling processes in Baselines' stds are shown in the table below. PGExplainer is a general model compatible with different GNNs and diverse learning tasks. Besides, instead of edge-level important scores, they just calculate node-level important scores. We select a method "Gradient" in the CVPR paper which doesn't require The AUC scores on BA-2motifs and MUT AG are 0.773 and 0.653, The KDD paper mentioned just showed up (June 3). Second, it only provides model-level explanations without preserving the local fidelity. That's why we call it a global method. As discussed in [38], "local fidelity" requires an explanation PGExplainer to preserve the "local fidelity", at the same time, with a global view of the GNN model. GNNExplainer is a pioneer to provide explanations for GNN's predictions. We include a parameterized network to enable explainer a global view of the GNN model. PGExplainer is much more effective and efficient than the state-of-the-art method. As discussed in Appendix D.1, "PGExplainer