Ismail Ilkan Ceylan
My research interests are broadly in AI & machine learning with a particular focus on relational learning & reasoning, which includes a class of challenging problems that can be naturally characterised on relational structures, such as graphs, knowledge bases, or more general logical representations. The goal is being able to more efficiently and reliably learn from relational patterns and reason over them. This is a highly interactive field, where techniques from knowledge representation (e.g., logical reasoning), machine learning (e.g., deep learning, graph representation learning, probabilistic inference), and theoretical computer science (complexity theory, graph theory) are relevant.
The Surprising Power of Graph Neural Networks with Random Node Initialization
Ralph Abboud‚ İsmail İlkan Ceylan‚ Martin Grohe and Thomas Lukasiewicz
In Proceedings of the 30th International Joint Conference on Artificial Intelligence‚ IJCAI 2021‚ August 21–26‚ 2021. IJCAI. August, 2021.
Open−World Probabilistic Databases: Semantics‚ Algorithms‚ Complexity
İsmail İlkan Ceylan‚ Adnan Darwiche and Guy Van den Broeck
In Artificial Intelligence. Vol. 295. 2021.
Preferred Explanations for Ontology−Mediated Queries under Existential Rules
İsmail İlkan Ceylan‚ Thomas Lukasiewicz‚ Enrico Malizia‚ Cristian Molinaro and Andrius Vaicenavičius
In Kevin Leyton−Brown and Mausam, editors, Proceedings of the 35th AAAI Conference on Artificial Intelligence‚ AAAI 2021‚ Virtual Conference‚ February 2–9‚ 2021. AAAI Press. 2021.