4 citations · 4 across the 3 of their papers we have counts for
4 papers · 1 filter
Efficient Algorithms for Learning Monophonic Halfspaces in Graphs
Marco Bressan, Emmanuel Esposito, Maximilian Thiessen
We study the problem of learning a binary classifier on the vertices of a graph. In particular, we consider classifiers given by monophonic halfspaces, partitions of the vertices t…
Expectation-Complete Graph Representations with Homomorphisms
Pascal Welke, Maximilian Thiessen, Fabian Jogl +1
We investigate novel random graph embeddings that can be computed in expected polynomial time and that are able to distinguish all non-isomorphic graphs in expectation. Previous gr…
Generalized Laplacian Positional Encoding for Graph Representation Learning
Sohir Maskey, Ali Parviz, Maximilian Thiessen +3
Graph neural networks (GNNs) are the primary tool for processing graph-structured data. Unfortunately, the most commonly used GNNs, called Message Passing Neural Networks (MPNNs) s…
Active Learning of Classifiers with Label and Seed Queries
Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi +2
We study exact active learning of binary and multiclass classifiers with margin. Given an -point set , we want to learn any unknown classifier on who…