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Pierre Osselin

2 accepted papers

2023

Bayesian Optimisation of Functions on Graphs

NeurIPS 2023poster

The increasing availability of graph-structured data motivates the task of optimising over functions defined on the node set of graphs. Traditional graph search algorithms can be applied in this case, but they may be sample-inefficient and do not make use of information about the function values; on…

Cited by 5SourcePDFScholar
2023

Structure-aware robustness certificates for graph classification

UAI 2023poster

Certifying the robustness of a graph-based machine learning model poses a critical challenge for safety. Current robustness certificates for graph classifiers guarantee output invariance with respect to the total number of node pair flips (edge addition or edge deletion), which amounts to an {$l_{0}…