Eitan, Yam

8 publications

ICML 2025 Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Joshua Southern, Yam Eitan, Guy Bar-Shalom, Michael M. Bronstein, Haggai Maron, Fabrizio Frasca

NeurIPS 2025 GradMetaNet: An Equivariant Architecture for Learning on Gradients Yoav Gelberg, Yam Eitan, Aviv Navon, Aviv Shamsian, Theo Putterman, Michael M. Bronstein, Haggai Maron

ICLRW 2025 GradMetaNet: An Equivariant Architecture for Learning on Gradients Yoav Gelberg, Yam Eitan, Aviv Navon, Aviv Shamsian, Theo Putterman, Haggai Maron

ICLR 2025 Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity Yam Eitan, Yoav Gelberg, Guy Bar-Shalom, Fabrizio Frasca, Michael M. Bronstein, Haggai Maron

NeurIPS 2024 A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening Guy Bar-Shalom, Yam Eitan, Fabrizio Frasca, Haggai Maron

NeurIPSW 2024 Efficient Subgraph GNNs via Graph Products and Coarsening Guy Bar-Shalom, Yam Eitan, Fabrizio Frasca, Haggai Maron

NeurIPSW 2024 Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity Yam Eitan, Yoav Gelberg, Guy Bar-Shalom, Fabrizio Frasca, Michael M. Bronstein, Haggai Maron

NeurIPSW 2022 Fair Synthetic Data Does Not Necessarily Lead to Fair Models Yam Eitan, Nathan Cavaglione, Michael Arbel, Samuel Cohen