Michael Bronstein

Publication Activity (10 Years)

Years Active: 2021-2024
Publications (10 Years): 8

Top Topics

Future Directions

Top Venues

J. Mach. Learn. Res.

Publications

Theodore Papamarkou, Tolga Birdal, Michael Bronstein, Gunnar Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Liò, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Velickovic, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi
Position Paper: Challenges and Opportunities in Topological Deep Learning. CoRR (2024)
Emily Jin, Michael Bronstein, Ismail Ilkan Ceylan, Matthias Lanzinger
Homomorphism Counts for Graph Neural Networks: All About That Basis. CoRR (2024)
Christopher Morris, Nadav Dym, Haggai Maron, Ismail Ilkan Ceylan, Fabrizio Frasca, Ron Levie, Derek Lim, Michael Bronstein, Martin Grohe, Stefanie Jegelka
Future Directions in Foundations of Graph Machine Learning. CoRR (2024)
Ben Finkelshtein, Xingyue Huang, Michael Bronstein, Ismail Ilkan Ceylan
Cooperative Graph Neural Networks. CoRR (2023)
Joshua Southern, Jeremy Wayland, Michael Bronstein, Bastian Rieck
Curvature Filtrations for Graph Generative Model Evaluation. NeurIPS (2023)
Qitian Wu, Chenxiao Yang, Kaipeng Zeng, Fan Nie, Michael Bronstein, Junchi Yan
Advective Diffusion Transformers for Topological Generalization in Graph Learning. CoRR (2023)
Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderón, Michael Bronstein, Marcello Restelli
Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization. CoRR (2022)
Ron Levie, Wei Huang, Lorenzo Bucci, Michael Bronstein, Gitta Kutyniok
Transferability of Spectral Graph Convolutional Neural Networks. J. Mach. Learn. Res. 22 (2021)