Haggai Maron

According to our database, Haggai Maron authored at least 75 papers between 2016 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Attention-guided self-supervised distinctive region detection in point clouds.

Yuval Onn, Haggai Maron, Ayellet Tal

Vis. Comput., July, 2025

GradMetaNet: An Equivariant Architecture for Learning on Gradients.

Yoav Gelberg, Yam Eitan, Aviv Navon, Aviv Shamsian, Theo Putterman, Michael M. Bronstein, Haggai Maron

CoRR, July, 2025

Understanding and Improving Laplacian Positional Encodings For Temporal GNNs.

Yaniv Galron, Fabrizio Frasca, Haggai Maron, Eran Treister, Moshe Eliasof

CoRR, June, 2025

It Takes a Graph to Know a Graph: Rewiring for Homophily with a Reference Graph.

Harel Mendelman, Haggai Maron, Ronen Talmon

CoRR, May, 2025

Efficient GNN Training Through Structure-Aware Randomized Mini-Batching.

Vignesh Balaji, Christos Kozyrakis, Gal Chechik, Haggai Maron

CoRR, April, 2025

Learning on LLM Output Signatures for gray-box LLM Behavior Analysis.

Guy Bar-Shalom, Fabrizio Frasca, Derek Lim, Yoav Gelberg, Yftah Ziser, Ran El-Yaniv, Gal Chechik, Haggai Maron

CoRR, March, 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

CoRR, January, 2025

Directed Graph Generation with Heat Kernels.

Marc T. Law, Karsten Kreis, Haggai Maron

Trans. Mach. Learn. Res., 2025

Foldable SuperNets: Scalable Merging of Transformers with Different Initializations and Tasks.

Edan Kinderman, Itay Hubara, Haggai Maron, Daniel Soudry

Trans. Mach. Learn. Res., 2025

Lightning-Fast Image Inversion and Editing for Text-to-Image Diffusion Models.

Dvir Samuel, Barak Meiri, Haggai Maron, Yoad Tewel, Nir Darshan, Shai Avidan, Gal Chechik, Rami Ben-Ari

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Homomorphism Expressivity of Spectral Invariant Graph Neural Networks.

Jingchu Gai, Yiheng Du, Bohang Zhang, Haggai Maron, Liwei Wang

Proceedings of the Thirteenth International Conference on Learning Representations, 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

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs.

Fabrizio Frasca, Fabian Jogl, Moshe Eliasof, Matan Ostrovsky, Carola-Bibiane Schönlieb, Thomas Gärtner, Haggai Maron

CoRR, 2024

On the Reconstruction of Training Data from Group Invariant Networks.

Ran Elbaz, Gilad Yehudai, Meirav Galun, Haggai Maron

CoRR, 2024

Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models.

Moe Putterman, Derek Lim, Yoav Gelberg, Stefanie Jegelka, Haggai Maron

CoRR, 2024

Topological Blind Spots: 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

CoRR, 2024

The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof.

Derek Lim, Moe Putterman, Robin Walters, Haggai Maron, Stefanie Jegelka

CoRR, 2024

Learning Priors for Non Rigid SfM from Casual Videos.

Yoni Kasten, Wuyue Lu, Haggai Maron

CoRR, 2024

Future Directions in Foundations of Graph Machine Learning.

Christopher Morris, Nadav Dym, Haggai Maron, Ismail Ilkan Ceylan, Fabrizio Frasca, Ron Levie, Derek Lim, Michael M. Bronstein, Martin Grohe, Stefanie Jegelka

CoRR, 2024

The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof.

Derek Lim, Theo Putterman, Robin Walters, Haggai Maron, Stefanie Jegelka

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Fast Encoder-Based 3D from Casual Videos via Point Track Processing.

Yoni Kasten, Wuyue Lu, Haggai Maron

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

GRANOLA: Adaptive Normalization for Graph Neural Networks.

Moshe Eliasof, Beatrice Bevilacqua, Carola-Bibiane Schönlieb, Haggai Maron

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening.

Guy Bar-Shalom, Yam Eitan, Fabrizio Frasca, Haggai Maron

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

On the Expressive Power of Spectral Invariant Graph Neural Networks.

Bohang Zhang, Lingxiao Zhao, Haggai Maron

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Improved Generalization of Weight Space Networks via Augmentations.

Aviv Shamsian, Aviv Navon, David W. Zhang, Yan Zhang, Ethan Fetaya, Gal Chechik, Haggai Maron

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Equivariant Deep Weight Space Alignment.

Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik, Nadav Dym, Haggai Maron

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products.

Guy Bar-Shalom, Beatrice Bevilacqua, Haggai Maron

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Position: Future Directions in the Theory of Graph Machine Learning.

Christopher Morris, Fabrizio Frasca, Nadav Dym, Haggai Maron, Ismail Ilkan Ceylan, Ron Levie, Derek Lim, Michael M. Bronstein, Martin Grohe, Stefanie Jegelka

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Graph Metanetworks for Processing Diverse Neural Architectures.

Derek Lim, Haggai Maron, Marc T. Law, Jonathan Lorraine, James Lucas

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Efficient Subgraph GNNs by Learning Effective Selection Policies.

Beatrice Bevilacqua, Moshe Eliasof, Eli A. Meirom, Bruno Ribeiro, Haggai Maron

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Weisfeiler and Leman go Machine Learning: The Story so far.

Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, Karsten M. Borgwardt

J. Mach. Learn. Res., 2023

Data Augmentations in Deep Weight Spaces.

Aviv Shamsian, David W. Zhang, Aviv Navon, Yan Zhang, Miltiadis Kofinas, Idan Achituve, Riccardo Valperga, Gertjan J. Burghouts, Efstratios Gavves, Cees G. M. Snoek, Ethan Fetaya, Gal Chechik, Haggai Maron

CoRR, 2023

Norm-guided latent space exploration for text-to-image generation.

Dvir Samuel, Rami Ben-Ari, Nir Darshan, Haggai Maron, Gal Chechik

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Expressive Sign Equivariant Networks for Spectral Geometric Learning.

Derek Lim, Joshua Robinson, Stefanie Jegelka, Haggai Maron

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Equivariant Polynomials for Graph Neural Networks.

Omri Puny, Derek Lim, Bobak Toussi Kiani, Haggai Maron, Yaron Lipman

Proceedings of the International Conference on Machine Learning, 2023

Equivariant Architectures for Learning in Deep Weight Spaces.

Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron

Proceedings of the International Conference on Machine Learning, 2023

Graph Positional Encoding via Random Feature Propagation.

Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik, Haggai Maron

Proceedings of the International Conference on Machine Learning, 2023

Sign and Basis Invariant Networks for Spectral Graph Representation Learning.

Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching.

Ali Taghibakhshi, Mingyuan Ma, Ashwath Aithal, Onur Yilmaz, Haggai Maron, Matthew West

Proceedings of the Big Data Analytics and Knowledge Discovery, 2023

2022

StyleGAN-NADA: CLIP-guided domain adaptation of image generators.

Rinon Gal, Or Patashnik, Haggai Maron, Amit H. Bermano, Gal Chechik, Daniel Cohen-Or

ACM Trans. Graph., 2022

Generalized Laplacian Positional Encoding for Graph Representation Learning.

Sohir Maskey, Ali Parviz, Maximilian Thiessen, Hannes Stärk, Ylli Sadikaj, Haggai Maron

CoRR, 2022

Federated Learning with Heterogeneous Architectures using Graph HyperNetworks.

Or Litany, Haggai Maron, David Acuna, Jan Kautz, Gal Chechik, Sanja Fidler

CoRR, 2022

A Simple and Universal Rotation Equivariant Point-Cloud Network.

Ben Finkelshtein, Chaim Baskin, Haggai Maron, Nadav Dym

Proceedings of the Topological, 2022

Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries.

Fabrizio Frasca, Beatrice Bevilacqua, Michael M. Bronstein, Haggai Maron

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Multi-Task Learning as a Bargaining Game.

Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya

Proceedings of the International Conference on Machine Learning, 2022

Optimizing Tensor Network Contraction Using Reinforcement Learning.

Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik

Proceedings of the International Conference on Machine Learning, 2022

Equivariant Subgraph Aggregation Networks.

Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators.

Rinon Gal, Or Patashnik, Haggai Maron, Gal Chechik, Daniel Cohen-Or

CoRR, 2021

Self-Supervised Learning for Domain Adaptation on Point Clouds.

Idan Achituve, Haggai Maron, Gal Chechik

Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021

Scene-Agnostic Multi-Microphone Speech Dereverberation.

Yochai Yemini, Ethan Fetaya, Haggai Maron, Sharon Gannot

Proceedings of the 22nd Annual Conference of the International Speech Communication Association, Interspeech 2021, Brno, Czechia, August 30, 2021

On Learning Sets of Symmetric Elements (Extended Abstract).

Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

From Local Structures to Size Generalization in Graph Neural Networks.

Gilad Yehudai, Ethan Fetaya, Eli A. Meirom, Gal Chechik, Haggai Maron

Proceedings of the 38th International Conference on Machine Learning, 2021

Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks.

Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik

Proceedings of the 38th International Conference on Machine Learning, 2021

Auxiliary Learning by Implicit Differentiation.

Aviv Navon, Idan Achituve, Haggai Maron, Gal Chechik, Ethan Fetaya

Proceedings of the 9th International Conference on Learning Representations, 2021

On the Universality of Rotation Equivariant Point Cloud Networks.

Nadav Dym, Haggai Maron

Proceedings of the 9th International Conference on Learning Representations, 2021

Deep Permutation Equivariant Structure from Motion.

Dror Moran, Hodaya Koslowsky, Yoni Kasten, Haggai Maron, Meirav Galun, Ronen Basri

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

Position-Agnostic Multi-Microphone Speech Dereverberation.

Yochai Yemini, Ethan Fetaya, Haggai Maron, Sharon Gannot

CoRR, 2020

On Size Generalization in Graph Neural Networks.

Gilad Yehudai, Ethan Fetaya, Eli A. Meirom, Gal Chechik, Haggai Maron

CoRR, 2020

How to Stop Epidemics: Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks.

Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik

CoRR, 2020

Set2Graph: Learning Graphs From Sets.

Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On Learning Sets of Symmetric Elements.

Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya

Proceedings of the 37th International Conference on Machine Learning, 2020

Learning Algebraic Multigrid Using Graph Neural Networks.

Ilay Luz, Meirav Galun, Haggai Maron, Ronen Basri, Irad Yavneh

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Sinkhorn Algorithm for Lifted Assignment Problems.

Yam Kushinsky, Haggai Maron, Nadav Dym, Yaron Lipman

SIAM J. Imaging Sci., 2019

Provably Powerful Graph Networks.

Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, Yaron Lipman

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Controlling Neural Level Sets.

Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On the Universality of Invariant Networks.

Haggai Maron, Ethan Fetaya, Nimrod Segol, Yaron Lipman

Proceedings of the 36th International Conference on Machine Learning, 2019

Invariant and Equivariant Graph Networks.

Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman

Proceedings of the 7th International Conference on Learning Representations, 2019

Surface Networks via General Covers.

Niv Haim, Nimrod Segol, Heli Ben-Hamu, Haggai Maron, Yaron Lipman

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

2018

Multi-chart generative surface modeling.

Heli Ben-Hamu, Haggai Maron, Itay Kezurer, Gal Avineri, Yaron Lipman

ACM Trans. Graph., 2018

Point convolutional neural networks by extension operators.

Matan Atzmon, Haggai Maron, Yaron Lipman

ACM Trans. Graph., 2018

(Probably) Concave Graph Matching.

Haggai Maron, Yaron Lipman

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Convolutional neural networks on surfaces via seamless toric covers.

Haggai Maron, Meirav Galun, Noam Aigerman, Miri Trope, Nadav Dym, Ersin Yumer, Vladimir G. Kim, Yaron Lipman

ACM Trans. Graph., 2017

DS++: a flexible, scalable and provably tight relaxation for matching problems.

Nadav Dym, Haggai Maron, Yaron Lipman

ACM Trans. Graph., 2017

2016

Point registration via efficient convex relaxation.

Haggai Maron, Nadav Dym, Itay Kezurer, Shahar Z. Kovalsky, Yaron Lipman

ACM Trans. Graph., 2016

Passive light and viewpoint sensitive display of 3D content.

Anat Levin, Haggai Maron, Michal Yarom

Proceedings of the 2016 IEEE International Conference on Computational Photography, 2016