Pascal Welke

Orcid: 0000-0002-2123-3781

Affiliations:
  • University of Bonn, Institute for Computer Science, Germany


According to our database1, Pascal Welke authored at least 30 papers between 2014 and 2024.

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Bibliography

2024
Logical Distillation of Graph Neural Networks.
CoRR, 2024

Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning.
CoRR, 2024

2023
Harnessing Prior Knowledge for Explainable Machine Learning: An Overview.
Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning, 2023

An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Expectation-Complete Graph Representations with Homomorphisms.
Proceedings of the International Conference on Machine Learning, 2023

Retention is All You Need.
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

A New Aligned Simple German Corpus.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

Hidden Schema Networks.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022
Machine learning framework to predict nonwoven material properties from fiber graph representations.
Softw. Impacts, December, 2022

A generalized Weisfeiler-Lehman graph kernel.
Mach. Learn., 2022

Hidden Schema Networks.
CoRR, 2022

Frequent Generalized Subgraph Mining via Graph Edit Distances.
Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022

Graph Filtration Kernels.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Explainable Machine Learning with Prior Knowledge: An Overview.
CoRR, 2021

SUSAN: The Structural Similarity Random Walk Kernel.
Proceedings of the 2021 SIAM International Conference on Data Mining, 2021

2020
Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks across Languages.
CoRR, 2020

HOPS: Probabilistic Subtree Mining for Small and Large Graphs.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Decision Snippet Features.
Proceedings of the 25th International Conference on Pattern Recognition, 2020

Efficient Frequent Subgraph Mining in Transactional Databases.
Proceedings of the 7th IEEE International Conference on Data Science and Advanced Analytics, 2020

2019
Efficient Frequent Subtree Mining Beyond Forests.
PhD thesis, 2019

Probabilistic and exact frequent subtree mining in graphs beyond forests.
Mach. Learn., 2019

2018
Probabilistic frequent subtrees for efficient graph classification and retrieval.
Mach. Learn., 2018

Mining Tree Patterns with Partially Injective Homomorphisms.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

2017
Simple Necessary Conditions for the Existence of a Hamiltonian Path with Applications to Cactus Graphs.
CoRR, 2017

2016
Differentiating smartphone users by app usage.
Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2016

Min-Hashing for Probabilistic Frequent Subtree Feature Spaces.
Proceedings of the Discovery Science - 19th International Conference, 2016

Ligand Affinity Prediction with Multi-pattern Kernels.
Proceedings of the Discovery Science - 19th International Conference, 2016

Three-hop distance estimation in social graphs.
Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016

2015
Probabilistic Frequent Subtree Kernels.
Proceedings of the New Frontiers in Mining Complex Patterns - 4th International Workshop, 2015

2014
On the Complexity of Frequent Subtree Mining in Very Simple Structures.
Proceedings of the Inductive Logic Programming - 24th International Conference, 2014


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