Weekend Reading

For those who are not busy with ICLR rebuttals — you can now have a look at all accepted NeurIPS’22 papers on OpenReview (we will have a review of graph papers at NeurIPS a bit later). Meanwhile, the week brought several cool new works:

Are Defenses for Graph Neural Networks Robust? by Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski. Probably THE most comprehensive work of 2022 on adversarial robustness of GNNs.

TuneUp: A Training Strategy for Improving Generalization of Graph Neural Networks by Weihua Hu, Kaidi Cao, Kexin Huang, Edward W Huang, Karthik Subbian, Jure Leskovec. The paper introduces a new self-supervised strategy by asking the model to generalize better on tail nodes of the graph after some synthetic edge dropout. Works in node classification, link prediction, and recsys.

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions by Nikolaos Karalias, Joshua David Robinson, Andreas Loukas, Stefanie Jegelka. Insightful theoretical work on set functions and discrete learning. Particularly good results on combinatorial optimization problems like max clique and max independent set.