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ORCIDHaggai Maron
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2020 – today
- 2024
[c37]Beatrice Bevilacqua, Moshe Eliasof, Eli A. Meirom, Bruno Ribeiro, Haggai Maron:
Efficient Subgraph GNNs by Learning Effective Selection Policies. ICLR 2024- [c36]Derek Lim, Haggai Maron, Marc T. Law, Jonathan Lorraine
, James Lucas:
Graph Metanetworks for Processing Diverse Neural Architectures. ICLR 2024 - [c35]Christopher Morris, Fabrizio Frasca, Nadav Dym, Haggai Maron, Ismail Ilkan Ceylan, Ron Levie, Derek Lim, Michael M. Bronstein, Martin Grohe, Stefanie Jegelka:
Position: Future Directions in the Theory of Graph Machine Learning. ICML 2024 - [c34]Guy Bar-Shalom, Beatrice Bevilacqua, Haggai Maron:
Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products. ICML 2024 - [c33]Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik, Nadav Dym, Haggai Maron:
Equivariant Deep Weight Space Alignment. ICML 2024 - [c32]Aviv Shamsian, Aviv Navon, David W. Zhang, Yan Zhang, Ethan Fetaya, Gal Chechik, Haggai Maron:
Improved Generalization of Weight Space Networks via Augmentations. ICML 2024 - [c31]Bohang Zhang, Lingxiao Zhao, Haggai Maron:
On the Expressive Power of Spectral Invariant Graph Neural Networks. ICML 2024 - [i49]Christopher Morris, Nadav Dym, Haggai Maron, Ismail Ilkan Ceylan, Fabrizio Frasca, Ron Levie, Derek Lim, Michael M. Bronstein, Martin Grohe, Stefanie Jegelka:
Future Directions in Foundations of Graph Machine Learning. CoRR abs/2402.02287 (2024) - [i48]Aviv Shamsian, Aviv Navon, David W. Zhang, Yan Zhang, Ethan Fetaya, Gal Chechik, Haggai Maron:
Improved Generalization of Weight Space Networks via Augmentations. CoRR abs/2402.04081 (2024) - [i47]Guy Bar-Shalom, Beatrice Bevilacqua, Haggai Maron:
Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products. CoRR abs/2402.08450 (2024) - [i46]Yoni Kasten, Wuyue Lu, Haggai Maron:
Learning Priors for Non Rigid SfM from Casual Videos. CoRR abs/2404.07097 (2024) - [i45]Moshe Eliasof, Beatrice Bevilacqua, Carola-Bibiane Schönlieb, Haggai Maron:
GRANOLA: Adaptive Normalization for Graph Neural Networks. CoRR abs/2404.13344 (2024) - [i44]Derek Lim, Moe Putterman, Robin Walters, Haggai Maron, Stefanie Jegelka:
The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof. CoRR abs/2405.20231 (2024) - [i43]Bohang Zhang, Lingxiao Zhao, Haggai Maron:
On the Expressive Power of Spectral Invariant Graph Neural Networks. CoRR abs/2406.04336 (2024) - [i42]Guy Bar-Shalom, Yam Eitan, Fabrizio Frasca, Haggai Maron:
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening. CoRR abs/2406.09291 (2024) - [i41]Yam Eitan, Yoav Gelberg, Guy Bar-Shalom, Fabrizio Frasca, Michael M. Bronstein, Haggai Maron:
Topological Blind Spots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity. CoRR abs/2408.05486 (2024) - [i40]Edan Kinderman, Itay Hubara, Haggai Maron, Daniel Soudry:
Foldable SuperNets: Scalable Merging of Transformers with Different Initializations and Tasks. CoRR abs/2410.01483 (2024) - [i39]Moe Putterman, Derek Lim, Yoav Gelberg, Stefanie Jegelka, Haggai Maron:
Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models. CoRR abs/2410.04207 (2024) - 2023
- [j8]Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, Karsten M. Borgwardt:
Weisfeiler and Leman go Machine Learning: The Story so far. J. Mach. Learn. Res. 24: 333:1-333:59 (2023) - [c30]Ali Taghibakhshi, Mingyuan Ma, Ashwath Aithal, Onur Yilmaz, Haggai Maron, Matthew West:
Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching. DaWaK 2023: 303-315 - [c29]Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka:
Sign and Basis Invariant Networks for Spectral Graph Representation Learning. ICLR 2023 - [c28]Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik, Haggai Maron:
Graph Positional Encoding via Random Feature Propagation. ICML 2023: 9202-9223 - [c27]Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron:
Equivariant Architectures for Learning in Deep Weight Spaces. ICML 2023: 25790-25816 - [c26]Omri Puny, Derek Lim, Bobak Toussi Kiani, Haggai Maron, Yaron Lipman:
Equivariant Polynomials for Graph Neural Networks. ICML 2023: 28191-28222 - [c25]Derek Lim, Joshua Robinson, Stefanie Jegelka, Haggai Maron:
Expressive Sign Equivariant Networks for Spectral Geometric Learning. NeurIPS 2023 - [c24]Dvir Samuel, Rami Ben-Ari, Nir Darshan, Haggai Maron, Gal Chechik:
Norm-guided latent space exploration for text-to-image generation. NeurIPS 2023 - [i38]Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron:
Equivariant Architectures for Learning in Deep Weight Spaces. CoRR abs/2301.12780 (2023) - [i37]Omri Puny, Derek Lim, Bobak Toussi Kiani, Haggai Maron, Yaron Lipman:
Equivariant Polynomials for Graph Neural Networks. CoRR abs/2302.11556 (2023) - [i36]Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik, Haggai Maron:
Graph Positional Encoding via Random Feature Propagation. CoRR abs/2303.02918 (2023) - [i35]Ali Taghibakhshi, Mingyuan Ma, Ashwath Aithal, Onur Yilmaz, Haggai Maron, Matthew West:
Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching. CoRR abs/2304.03215 (2023) - [i34]Dvir Samuel, Rami Ben-Ari, Nir Darshan, Haggai Maron, Gal Chechik:
Norm-guided latent space exploration for text-to-image generation. CoRR abs/2306.08687 (2023) - [i33]Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik, Nadav Dym, Haggai Maron:
Equivariant Deep Weight Space Alignment. CoRR abs/2310.13397 (2023) - [i32]Beatrice Bevilacqua, Moshe Eliasof, Eli A. Meirom, Bruno Ribeiro, Haggai Maron:
Efficient Subgraph GNNs by Learning Effective Selection Policies. CoRR abs/2310.20082 (2023) - [i31]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:
Data Augmentations in Deep Weight Spaces. CoRR abs/2311.08851 (2023) - [i30]Derek Lim, Joshua Robinson, Stefanie Jegelka, Haggai Maron:
Expressive Sign Equivariant Networks for Spectral Geometric Learning. CoRR abs/2312.02339 (2023) - [i29]Derek Lim, Haggai Maron, Marc T. Law, Jonathan Lorraine, James Lucas:
Graph Metanetworks for Processing Diverse Neural Architectures. CoRR abs/2312.04501 (2023) - 2022
- [j7]Rinon Gal, Or Patashnik, Haggai Maron, Amit H. Bermano, Gal Chechik, Daniel Cohen-Or:
StyleGAN-NADA: CLIP-guided domain adaptation of image generators. ACM Trans. Graph. 41(4): 141:1-141:13 (2022) - [c23]Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron:
Equivariant Subgraph Aggregation Networks. ICLR 2022 - [c22]Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik:
Optimizing Tensor Network Contraction Using Reinforcement Learning. ICML 2022: 15278-15292 - [c21]Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya:
Multi-Task Learning as a Bargaining Game. ICML 2022: 16428-16446 - [c20]Fabrizio Frasca, Beatrice Bevilacqua, Michael M. Bronstein, Haggai Maron:
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries. NeurIPS 2022 - [c19]Ben Finkelshtein, Chaim Baskin, Haggai Maron, Nadav Dym:
A Simple and Universal Rotation Equivariant Point-Cloud Network. TAG-ML 2022: 107-115 - [i28]Or Litany, Haggai Maron, David Acuna, Jan Kautz, Gal Chechik, Sanja Fidler:
Federated Learning with Heterogeneous Architectures using Graph HyperNetworks. CoRR abs/2201.08459 (2022) - [i27]Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya:
Multi-Task Learning as a Bargaining Game. CoRR abs/2202.01017 (2022) - [i26]Derek Lim, Joshua Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka:
Sign and Basis Invariant Networks for Spectral Graph Representation Learning. CoRR abs/2202.13013 (2022) - [i25]Ben Finkelshtein, Chaim Baskin, Haggai Maron, Nadav Dym:
A Simple and Universal Rotation Equivariant Point-cloud Network. CoRR abs/2203.01216 (2022) - [i24]Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik:
Optimizing Tensor Network Contraction Using Reinforcement Learning. CoRR abs/2204.09052 (2022) - [i23]Fabrizio Frasca, Beatrice Bevilacqua, Michael M. Bronstein, Haggai Maron:
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries. CoRR abs/2206.11140 (2022) - [i22]Sohir Maskey, Ali Parviz, Maximilian Thiessen, Hannes Stärk, Ylli Sadikaj, Haggai Maron:
Generalized Laplacian Positional Encoding for Graph Representation Learning. CoRR abs/2210.15956 (2022) - 2021
- [c18]Dror Moran, Hodaya Koslowsky, Yoni Kasten, Haggai Maron, Meirav Galun, Ronen Basri:
Deep Permutation Equivariant Structure from Motion. ICCV 2021: 5956-5966 - [c17]Nadav Dym, Haggai Maron:
On the Universality of Rotation Equivariant Point Cloud Networks. ICLR 2021 - [c16]Aviv Navon, Idan Achituve, Haggai Maron, Gal Chechik, Ethan Fetaya:
Auxiliary Learning by Implicit Differentiation. ICLR 2021 - [c15]Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik:
Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks. ICML 2021: 7565-7577 - [c14]Gilad Yehudai, Ethan Fetaya, Eli A. Meirom, Gal Chechik, Haggai Maron:
From Local Structures to Size Generalization in Graph Neural Networks. ICML 2021: 11975-11986 - [c13]Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya:
On Learning Sets of Symmetric Elements (Extended Abstract). IJCAI 2021: 4794-4798 - [c12]Yochai Yemini, Ethan Fetaya, Haggai Maron, Sharon Gannot:
Scene-Agnostic Multi-Microphone Speech Dereverberation. Interspeech 2021: 1129-1133 - [c11]Idan Achituve, Haggai Maron, Gal Chechik:
Self-Supervised Learning for Domain Adaptation on Point Clouds. WACV 2021: 123-133 - [i21]Dror Moran, Hodaya Koslowsky, Yoni Kasten, Haggai Maron, Meirav Galun, Ronen Basri:
Deep Permutation Equivariant Structure from Motion. CoRR abs/2104.06703 (2021) - [i20]Rinon Gal, Or Patashnik, Haggai Maron, Gal Chechik, Daniel Cohen-Or:
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators. CoRR abs/2108.00946 (2021) - [i19]Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron:
Equivariant Subgraph Aggregation Networks. CoRR abs/2110.02910 (2021) - [i18]Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, Karsten M. Borgwardt:
Weisfeiler and Leman go Machine Learning: The Story so far. CoRR abs/2112.09992 (2021) - 2020
- [c10]Ilay Luz, Meirav Galun, Haggai Maron, Ronen Basri, Irad Yavneh:
Learning Algebraic Multigrid Using Graph Neural Networks. ICML 2020: 6489-6499 - [c9]Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya:
On Learning Sets of Symmetric Elements. ICML 2020: 6734-6744 - [c8]Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman:
Set2Graph: Learning Graphs From Sets. NeurIPS 2020 - [i17]Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya:
On Learning Sets of Symmetric Elements. CoRR abs/2002.08599 (2020) - [i16]Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman:
Set2Graph: Learning Graphs From Sets. CoRR abs/2002.08772 (2020) - [i15]Ilay Luz, Meirav Galun, Haggai Maron, Ronen Basri, Irad Yavneh:
Learning Algebraic Multigrid Using Graph Neural Networks. CoRR abs/2003.05744 (2020) - [i14]Idan Achituve, Haggai Maron, Gal Chechik:
Self-Supervised Learning for Domain Adaptation on Point-Clouds. CoRR abs/2003.12641 (2020) - [i13]Aviv Navon, Idan Achituve, Haggai Maron, Gal Chechik, Ethan Fetaya:
Auxiliary Learning by Implicit Differentiation. CoRR abs/2007.02693 (2020) - [i12]Nadav Dym, Haggai Maron:
On the Universality of Rotation Equivariant Point Cloud Networks. CoRR abs/2010.02449 (2020) - [i11]Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik:
How to Stop Epidemics: Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks. CoRR abs/2010.05313 (2020) - [i10]Gilad Yehudai, Ethan Fetaya, Eli A. Meirom, Gal Chechik, Haggai Maron:
On Size Generalization in Graph Neural Networks. CoRR abs/2010.08853 (2020) - [i9]Yochai Yemini, Ethan Fetaya, Haggai Maron, Sharon Gannot:
Position-Agnostic Multi-Microphone Speech Dereverberation. CoRR abs/2010.11875 (2020)
2010 – 2019
- 2019
- [j6]Yam Kushinsky, Haggai Maron, Nadav Dym, Yaron Lipman:
Sinkhorn Algorithm for Lifted Assignment Problems. SIAM J. Imaging Sci. 12(2): 716-735 (2019) - [c7]Niv Haim, Nimrod Segol, Heli Ben-Hamu, Haggai Maron, Yaron Lipman:
Surface Networks via General Covers. ICCV 2019: 632-641 - [c6]Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman:
Invariant and Equivariant Graph Networks. ICLR (Poster) 2019 - [c5]Haggai Maron, Ethan Fetaya, Nimrod Segol, Yaron Lipman:
On the Universality of Invariant Networks. ICML 2019: 4363-4371 - [c4]Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman:
Controlling Neural Level Sets. NeurIPS 2019: 2032-2041 - [c3]Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, Yaron Lipman:
Provably Powerful Graph Networks. NeurIPS 2019: 2153-2164 - [i8]Haggai Maron, Ethan Fetaya, Nimrod Segol, Yaron Lipman:
On the Universality of Invariant Networks. CoRR abs/1901.09342 (2019) - [i7]Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, Yaron Lipman:
Provably Powerful Graph Networks. CoRR abs/1905.11136 (2019) - [i6]Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman:
Controlling Neural Level Sets. CoRR abs/1905.11911 (2019) - 2018
- [j5]Matan Atzmon, Haggai Maron, Yaron Lipman:
Point convolutional neural networks by extension operators. ACM Trans. Graph. 37(4): 71 (2018) - [j4]Heli Ben-Hamu, Haggai Maron, Itay Kezurer, Gal Avineri, Yaron Lipman:
Multi-chart generative surface modeling. ACM Trans. Graph. 37(6): 215 (2018) - [c2]Haggai Maron, Yaron Lipman:
(Probably) Concave Graph Matching. NeurIPS 2018: 406-416 - [i5]Matan Atzmon, Haggai Maron, Yaron Lipman:
Point Convolutional Neural Networks by Extension Operators. CoRR abs/1803.10091 (2018) - [i4]Heli Ben-Hamu, Haggai Maron, Itay Kezurer, Gal Avineri, Yaron Lipman:
Multi-chart Generative Surface Modeling. CoRR abs/1806.02143 (2018) - [i3]Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman:
Invariant and Equivariant Graph Networks. CoRR abs/1812.09902 (2018) - [i2]Niv Haim, Nimrod Segol, Heli Ben-Hamu, Haggai Maron, Yaron Lipman:
Surface Networks via General Covers. CoRR abs/1812.10705 (2018) - 2017
- [j3]Haggai Maron, Meirav Galun, Noam Aigerman, Miri Trope, Nadav Dym, Ersin Yumer, Vladimir G. Kim, Yaron Lipman:
Convolutional neural networks on surfaces via seamless toric covers. ACM Trans. Graph. 36(4): 71:1-71:10 (2017) - [j2]Nadav Dym, Haggai Maron, Yaron Lipman:
DS++: a flexible, scalable and provably tight relaxation for matching problems. ACM Trans. Graph. 36(6): 184:1-184:14 (2017) - [i1]Nadav Dym, Haggai Maron, Yaron Lipman:
DS++: A flexible, scalable and provably tight relaxation for matching problems. CoRR abs/1705.06148 (2017) - 2016
- [j1]Haggai Maron, Nadav Dym, Itay Kezurer, Shahar Z. Kovalsky, Yaron Lipman:
Point registration via efficient convex relaxation. ACM Trans. Graph. 35(4): 73:1-73:12 (2016) - [c1]Anat Levin, Haggai Maron, Michal Yarom:
Passive light and viewpoint sensitive display of 3D content. ICCP 2016: 1-15
Coauthor Index
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