Michael M. Bronstein
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
95
Venues
18
Active years
2003–2025
Best venue rank
A*
Where they publish
Papers
95 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICLR | Fully-inductive Node Classification on Arbitrary Graphs. | Jianan Zhao, Zhaocheng Zhu, Mikhail Galkin, Hesham Mostafa, Michael M. Bronstein, Jian Tang |
| 2025 | ICLR | Bundle Neural Network for message diffusion on graphs. | Jacob Bamberger, Federico Barbero, Xiaowen Dong, Michael M. Bronstein |
| 2025 | ICLR | Homomorphism Counts as Structural Encodings for Graph Learning. | Linus Bao, Emily Jin, Michael M. Bronstein, Ismail Ilkan Ceylan, Matthias Lanzinger |
| 2025 | ICLR | Neural Spacetimes for DAG Representation Learning. | Haitz Sez de Ocriz Borde, Anastasis Kratsios, Marc T. Law, Xiaowen Dong, Michael M. Bronstein |
| 2025 | ICLR | 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 |
| 2025 | ICLR | Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction. | Jarrid Rector-Brooks, Mohsin Hasan, Zhangzhi Peng, Cheng-Hao Liu, Sarthak Mittal, Nouha Dziri, Michael M. Bronstein, Pranam Chatterjee, Alexander Tong, Joey Bose |
| 2025 | ICLR | Multi-domain Distribution Learning for De Novo Drug Design. | Arne Schneuing, Ilia Igashov, Adrian W. Dobbelstein, Thomas Castiglione, Michael M. Bronstein, Bruno Correia |
| 2025 | ICLR | Understanding Virtual Nodes: Oversquashing and Node Heterogeneity. | Joshua Southern, Francesco Di Giovanni, Michael M. Bronstein, Johannes F. Lutzeyer |
| 2025 | ICML | A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition. | Nian Liu, Xiaoxin He, Thomas Laurent, Francesco Di Giovanni, Michael M. Bronstein, Xavier Bresson |
| 2025 | ICML | On Measuring Long-Range Interactions in Graph Neural Networks. | Jacob Bamberger, Benjamin Gutteridge, Scott le Roux, Michael M. Bronstein, Xiaowen Dong |
| 2025 | ICML | Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks. | Maya Bechler-Speicher, Ben Finkelshtein, Fabrizio Frasca, Luis Mller, Jan Tnshoff, Antoine Siraudin, Viktor Zaverkin, Michael M. Bronstein, Mathias Niepert, Bryan Perozzi, Mikhail Galkin, Christopher Morris |
| 2025 | ICML | How Expressive are Knowledge Graph Foundation Models? | Xingyue Huang, Pablo Barcel, Michael M. Bronstein, Ismail Ilkan Ceylan, Mikhail Galkin, Juan L. Reutter, Miguel A. Romero Orth |
| 2025 | ICML | Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality. | Joshua Southern, Yam Eitan, Guy Bar-Shalom, Michael M. Bronstein, Haggai Maron, Fabrizio Frasca |
| 2025 | ICML | Scalable Equilibrium Sampling with Sequential Boltzmann Generators. | Charlie B. Tan, Joey Bose, Chen Lin, Leon Klein, Michael M. Bronstein, Alexander Tong |
| 2025 | ICML | Supercharging Graph Transformers with Advective Diffusion. | Qitian Wu, Chenxiao Yang, Kaipeng Zeng, Michael M. Bronstein |
| 2025 | KDD | Temporal Graph Learning Workshop. | Shenyang Huang, Daniele Zambon, Andrea Cini, Farimah Poursafaei, Jacob Chmura, Julia Gastinger, Reihaneh Rabbany, Michael M. Bronstein |
| 2024 | ICLR | Locality-Aware Graph Rewiring in GNNs. | Federico Barbero, Ameya Velingker, Amin Saberi, Michael M. Bronstein, Francesco Di Giovanni |
| 2024 | ICLR | From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module. | Claudio Battiloro, Indro Spinelli, Lev Telyatnikov, Michael M. Bronstein, Simone Scardapane, Paolo Di Lorenzo |
| 2024 | ICLR | SE(3)-Stochastic Flow Matching for Protein Backbone Generation. | Avishek Joey Bose, Tara Akhound-Sadegh, Guillaume Huguet, Kilian Fatras, Jarrid Rector-Brooks, Cheng-Hao Liu, Andrei Cristian Nica, Maksym Korablyov, Michael M. Bronstein, Alexander Tong |
| 2024 | ICLR | RetroBridge: Modeling Retrosynthesis with Markov Bridges. | Ilia Igashov, Arne Schneuing, Marwin H. S. Segler, Michael M. Bronstein, Bruno E. Correia |
| 2024 | ICML | 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 |
| 2024 | ICML | Cooperative Graph Neural Networks. | Ben Finkelshtein, Xingyue Huang, Michael M. Bronstein, Ismail Ilkan Ceylan |
| 2024 | ICML | Homomorphism Counts for Graph Neural Networks: All About That Basis. | Emily Jin, Michael M. Bronstein, Ismail Ilkan Ceylan, Matthias Lanzinger |
| 2024 | ICML | Position: Topological Deep Learning is the New Frontier for Relational Learning. | Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Lio, 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 |
| 2024 | WWW | The 1st International Workshop on Graph Foundation Models (GFM). | Haitao Mao, Jianan Zhao, Xiaoxin He, Zhikai Chen, Qian Huang, Zhaocheng Zhu, Jian Tang, Michael M. Bronstein, Xavier Bresson, Bryan Hooi, Haiyang Zhang, Xianfeng Tang, Luo Chen, Jiliang Tang |
| 2024 | UAI | To smooth a cloud or to pin it down: Expressiveness guarantees and insights on score matching in denoising diffusion models. | Teodora Reu, Francisco Vargas, Anna Kerekes, Michael M. Bronstein |
| 2023 | AAAI | Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization. | Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Caldern, Michael M. Bronstein, Marcello Restelli |
| 2023 | ICLR | Hyperbolic Deep Reinforcement Learning. | Edoardo Cetin, Benjamin Paul Chamberlain, Michael M. Bronstein, Jonathan J. Hunt |
| 2023 | ICLR | Graph Neural Networks for Link Prediction with Subgraph Sketching. | Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Yannick Hammerla, Michael M. Bronstein, Max Hansmire |
| 2023 | ICLR | Gradient Gating for Deep Multi-Rate Learning on Graphs. | T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra |
| 2023 | ICML | On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology. | Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio, Michael M. Bronstein |
| 2023 | ICML | DRew: Dynamically Rewired Message Passing with Delay. | Benjamin Gutteridge, Xiaowen Dong, Michael M. Bronstein, Francesco Di Giovanni |
| 2022 | ICLR | Equivariant Subgraph Aggregation Networks. | Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron |
| 2022 | ICLR | Understanding over-squashing and bottlenecks on graphs via curvature. | Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein |
| 2022 | ICML | Learning to Infer Structures of Network Games. | Emanuele Rossi, Federico Monti, Yan Leng, Michael M. Bronstein, Xiaowen Dong |
| 2022 | ICML | Graph-Coupled Oscillator Networks. | T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein |
| 2022 | KDD | Graph-based Representation Learning for Web-scale Recommender Systems. | Ahmed El-Kishky, Michael M. Bronstein, Ying Xiao, Aria Haghighi |
| 2021 | CVPR | Fast End-to-End Learning on Protein Surfaces. | Freyr Sverrisson, Jean Feydy, Bruno E. Correia, Michael M. Bronstein |
| 2021 | ICML | GRAND: Graph Neural Diffusion. | Ben Chamberlain, James Rowbottom, Maria I. Gorinova, Michael M. Bronstein, Stefan Webb, Emanuele Rossi |
| 2021 | ICML | Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. | Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F. Montfar, Pietro Li, Michael M. Bronstein |
| 2021 | MICCAI | Unsupervised Diffeomorphic Surface Registration and Non-linear Modelling. | Balder Croquet, Daan Christiaens, Seth M. Weinberg, Michael M. Bronstein, Dirk Vandermeulen, Peter Claes |
| 2021 | RecSys | RecSys 2021 Challenge Workshop: Fairness-aware engagement prediction at scale on Twitter's Home Timeline. | Vito Walter Anelli, Saikishore Kalloori, Bruce Ferwerda, Luca Belli, Alykhan Tejani, Frank Portman, Alexandre Lung-Yut-Fong, Ben Chamberlain, Yuanpu Xie, Jonathan Hunt, Michael M. Bronstein, Wenzhe Shi |
| 2021 | RecSys | The 2021 RecSys Challenge Dataset: Fairness is not optional. | Luca Belli, Alykhan Tejani, Frank Portman, Alexandre Lung-Yut-Fong, Ben Chamberlain, Yuanpu Xie, Kristian Lum, Jonathan Hunt, Michael M. Bronstein, Vito Walter Anelli, Saikishore Kalloori, Bruce Ferwerda, Wenzhe Shi |
| 2021 | RecSys | GReS: Workshop on Graph Neural Networks for Recommendation and Search. | Thibaut Thonet, Stphane Clinchant, Carlos Lassance, Elvin Isufi, Jiaqi Ma, Yutong Xie, Jean-Michel Renders, Michael M. Bronstein |
| 2020 | CVPR | Geometrically Principled Connections in Graph Neural Networks. | Shunwang Gong, Mehdi Bahri, Michael M. Bronstein, Stefanos Zafeiriou |
| 2020 | CVPR | Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild. | Dominik Kulon, Riza Alp Gler, Iasonas Kokkinos, Michael M. Bronstein, Stefanos Zafeiriou |
| 2020 | ECCV | The Average Mixing Kernel Signature. | Luca Cosmo, Giorgia Minello, Michael M. Bronstein, Luca Rossi, Andrea Torsello |
| 2020 | ICPR | 3D Facial Matching by Spiral Convolutional Metric Learning and a Biometric Fusion-Net of Demographic Properties. | Soha Sadat Mahdi, Nele Nauwelaers, Philip Joris, Giorgos Bouritsas, Shunwang Gong, Sergiy Bokhnyak, Susan Walsh, Mark D. Shriver, Michael M. Bronstein, Peter Claes |
| 2020 | MICCAI | Latent-Graph Learning for Disease Prediction. | Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, Michael M. Bronstein |
| 2020 | RecSys | RecSys 2020 Challenge Workshop: Engagement Prediction on Twitter's Home Timeline. | Vito Walter Anelli, Amra Delic, Gabriele Sottocornola, Jessie Smith, Nazareno Andrade, Luca Belli, Michael M. Bronstein, Akshay Gupta, Sofia Ira Ktena, Alexandre Lung-Yut-Fong, Frank Portman, Alykhan Tejani, Yuanpu Xie, Xiao Zhu, Wenzhe Shi |
| 2020 | RecSys | Tuning Word2vec for Large Scale Recommendation Systems. | Benjamin Paul Chamberlain, Emanuele Rossi, Dan Shiebler, Suvash Sedhain, Michael M. Bronstein |
| 2019 | BMVC | Single Image 3D Hand Reconstruction with Mesh Convolutions. | Dominik Kulon, Haoyang Wang, Riza Alp Gler, Michael M. Bronstein, Stefanos Zafeiriou |
| 2019 | CVPR | Isospectralization, or How to Hear Shape, Style, and Correspondence. | Luca Cosmo, Mikhail Panine, Arianna Rampini, Maks Ovsjanikov, Michael M. Bronstein, Emanuele Rodol |
| 2019 | CVPR | GFrames: Gradient-Based Local Reference Frame for 3D Shape Matching. | Simone Melzi, Riccardo Spezialetti, Federico Tombari, Michael M. Bronstein, Luigi Di Stefano, Emanuele Rodol |
| 2019 | ICCV | Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation. | Giorgos Bouritsas, Sergiy Bokhnyak, Stylianos Ploumpis, Stefanos Zafeiriou, Michael M. Bronstein |
| 2019 | ICLR | PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks. | Jan Svoboda, Jonathan Masci, Federico Monti, Michael M. Bronstein, Leonidas J. Guibas |
| 2018 | CVPR | Deformable Shape Completion With Graph Convolutional Autoencoders. | Or Litany, Alexander M. Bronstein, Michael M. Bronstein, Ameesh Makadia |
| 2018 | ICASSP | Deep Geometric Matrix Completion: A New Way for Recommender Systems. | Federico Monti, Michael M. Bronstein, Xavier Bresson |
| 2018 | ICMLA | Graph Neural Networks for IceCube Signal Classification. | Nicholas Choma, Federico Monti, Lisa Gerhardt, Tomasz Palczewski, Zahra Ronaghi, Prabhat, Wahid Bhimji, Michael M. Bronstein, Spencer R. Klein, Joan Bruna |
| 2018 | SGP | Functional Maps on Product Manifolds. | Emanuele Rodol, Zorah Lhner, Alex M. Bronstein, Michael M. Bronstein, Justin Solomon |
| 2017 | CVPR | Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs. | Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodol, Jan Svoboda, Michael M. Bronstein |
| 2017 | ICCV | Deep Functional Maps: Structured Prediction for Dense Shape Correspondence. | Or Litany, Tal Remez, Emanuele Rodol, Alexander M. Bronstein, Michael M. Bronstein |
| 2017 | SIGGRAPH | Computing and processing correspondences with functional maps. | Maks Ovsjanikov, Etienne Corman, Michael M. Bronstein, Emanuele Rodol, Mirela Ben-Chen, Leonidas J. Guibas, Frdric Chazal, Alexander M. Bronstein |
| 2017 | SGP | Localized Manifold Harmonics for Spectral Shape Analysis. | Simone Melzi, Emanuele Rodol, Umberto Castellani, Michael M. Bronstein |
| 2016 | CVPR | Efficient Globally Optimal 2D-to-3D Deformable Shape Matching. | Zorah Lhner, Emanuele Rodol, Frank R. Schmidt, Michael M. Bronstein, Daniel Cremers |
| 2016 | ECCV | MADMM: A Generic Algorithm for Non-smooth Optimization on Manifolds. | Artiom Kovnatsky, Klaus Glashoff, Michael M. Bronstein |
| 2016 | ICPR | Palmprint recognition via discriminative index learning. | Jan Svoboda, Jonathan Masci, Michael M. Bronstein |
| 2016 | SIGGRAPH | Geometric deep learning. | Jonathan Masci, Emanuele Rodol, Davide Boscaini, Michael M. Bronstein, Hao Li |
| 2016 | SIGGRAPH | Computing and processing correspondences with functional maps. | Maks Ovsjanikov, Etienne Corman, Michael M. Bronstein, Emanuele Rodol, Mirela Ben-Chen, Leonidas J. Guibas, Frdric Chazal, Alexander M. Bronstein |
| 2015 | CVPR | Functional correspondence by matrix completion. | Artiom Kovnatsky, Michael M. Bronstein, Xavier Bresson, Pierre Vandergheynst |
| 2015 | ICCV | Robust Principal Component Analysis on Graphs. | Nauman Shahid, Vassilis Kalofolias, Xavier Bresson, Michael M. Bronstein, Pierre Vandergheynst |
| 2014 | ICIP | Gamut mapping with image Laplacian commutators. | Artiom Kovnatsky, Davide Eynard, Michael M. Bronstein |
| 2013 | ICASSP | Learnable low rank sparse models for speech denoising. | Pablo Sprechmann, Alexander M. Bronstein, Michael M. Bronstein, Guillermo Sapiro |
| 2012 | CVPR | Intrinsic shape context descriptors for deformable shapes. | Iasonas Kokkinos, Michael M. Bronstein, Roee Litman, Alexander M. Bronstein |
| 2012 | ECCV | Stable Spectral Mesh Filtering. | Artiom Kovnatsky, Michael M. Bronstein, Alexander M. Bronstein |
| 2012 | ECCV | Putting the Pieces Together: Regularized Multi-part Shape Matching. | Or Litany, Alexander M. Bronstein, Michael M. Bronstein |
| 2012 | ECCV | Group-Valued Regularization for Analysis of Articulated Motion. | Guy Rosman, Alexander M. Bronstein, Michael M. Bronstein, Xue-Cheng Tai, Ron Kimmel |
| 2011 | CVPR | Affine-invariant diffusion geometry for the analysis of deformable 3D shapes. | Dan Raviv, Michael M. Bronstein, Alexander M. Bronstein, Ron Kimmel, Nir A. Sochen |
| 2010 | CVPR | Data fusion through cross-modality metric learning using similarity-sensitive hashing. | Michael M. Bronstein, Alexander M. Bronstein, Fabrice Michel, Nikos Paragios |
| 2010 | CVPR | Scale-invariant heat kernel signatures for non-rigid shape recognition. | Michael M. Bronstein, Iasonas Kokkinos |
| 2010 | ECCV | Spatially-Sensitive Affine-Invariant Image Descriptors. | Alexander M. Bronstein, Michael M. Bronstein |
| 2010 | ECCV | Intrinsic Regularity Detection in 3D Geometry. | Niloy J. Mitra, Alexander M. Bronstein, Michael M. Bronstein |
| 2008 | CVPR | Not only size matters: Regularized partial matching of nonrigid shapes. | Alexander M. Bronstein, Michael M. Bronstein |
| 2008 | ECCV | Regularized Partial Matching of Rigid Shapes. | Alexander M. Bronstein, Michael M. Bronstein |
| 2007 | ICCV | Rock, Paper, and Scissors: extrinsic vs. intrinsic similarity of non-rigid shapes. | Alexander M. Bronstein, Michael M. Bronstein, Ron Kimmel |
| 2007 | ICCV | Symmetries of non-rigid shapes. | Dan Raviv, Alexander M. Bronstein, Michael M. Bronstein, Ron Kimmel |
| 2006 | ECCV | Robust Expression-Invariant Face Recognition from Partially Missing Data. | Alexander M. Bronstein, Michael M. Bronstein, Ron Kimmel |
| 2005 | ICIP | Expression-invariant face recognition via spherical embedding. | Alexander M. Bronstein, Michael M. Bronstein, Ron Kimmel |
| 2005 | ICIP | "Unmixing" tissues: sparse component analysis in multi-contrast MRI. | Alexander M. Bronstein, Michael M. Bronstein, Michael Zibulevsky, Yehoshua Y. Zeevi |
| 2004 | ECCV | Face Recognition from Facial Surface Metric. | Alexander M. Bronstein, Michael M. Bronstein, Alon Spira, Ron Kimmel |
| 2004 | ICIP | Fusion of 2D and 3D data in three-dimensional face recognition. | Alexander M. Bronstein, Michael M. Bronstein, Eyal Gordon, Ron Kimmel |
| 2004 | ICIP | Optimal sparse representations for blind source separation and blind deconvolution: a learning approach. | Michael M. Bronstein, Alexander M. Bronstein, Michael Zibulevsky, Yehoshua Y. Zeevi |
| 2004 | ICIP | Fast relative newton algorithm for blind deconvolution of images. | Alexander M. Bronstein, Michael Zibulevsky, Michael M. Bronstein, Yehoshua Y. Zeevi |
| 2003 | ICASSP | Separation of semireflective layers using sparse ICA. | Alexander M. Bronstein, Michael M. Bronstein, Michael Zibulevsky, Yehoshua Y. Zeevi |
| 2003 | ICIP | Separation of reflections via sparse ICA. | Michael M. Bronstein, Alexander M. Bronstein, Michael Zibulevsky, Yehoshua Y. Zeevi |