150 citations · 431 across the 41 of their papers we have counts for
5 papers · 1 filter
Approximation Ratios of Graph Neural Networks for Combinatorial Problems
Ryoma Sato, Makoto Yamada, Hisashi Kashima
In this paper, from a theoretical perspective, we study how powerful graph neural networks (GNNs) can be for learning approximation algorithms for combinatorial problems. To this e…
Topological Bayesian Optimization with Persistence Diagrams
Tatsuya Shiraishi, Tam Le, Hisashi Kashima +1
Finding an optimal parameter of a black-box function is important for searching stable material structures and finding optimal neural network structures, and Bayesian optimization…
Learning to Sample Hard Instances for Graph Algorithms
Ryoma Sato, Makoto Yamada, Hisashi Kashima
Hard instances, which require a long time for a specific algorithm to solve, help (1) analyze the algorithm for accelerating it and (2) build a good benchmark for evaluating the pe…
Theoretical evidence for adversarial robustness through randomization
Rafael Pinot, Laurent Meunier, Alexandre Araujo +4
This paper investigates the theory of robustness against adversarial attacks. It focuses on the family of randomization techniques that consist in injecting noise in the network at…
Constant Time Graph Neural Networks
Ryoma Sato, Makoto Yamada, Hisashi Kashima
The recent advancements in graph neural networks (GNNs) have led to state-of-the-art performances in various applications, including chemo-informatics, question-answering systems,…