Bronstein, Michael M.

145 publications

TMLR 2026 A Survey on Over-Smoothing and Over-Squashing: Unified Propagation Perspectives on Graph Neural Networks and Transformers Alvaro Arroyo, Federico Barbero, Hugh Blayney, Michael M. Bronstein, Xiaowen Dong, Pietro Lio, Razvan Pascanu, Pierre Vandergheynst

ICLR 2026 Attention Sinks and Compression Valleys in LLMs Are Two Sides of the Same Coin Enrique Queipo-de-Llano, Alvaro Arroyo, Federico Barbero, Xiaowen Dong, Michael M. Bronstein, Yann LeCun, Ravid Shwartz-Ziv

ICLR 2026 Carré Du Champ Flow Matching: Better Quality-Generalisation Tradeoff in Generative Models Jacob Bamberger, Iolo Jones, Dennis Duncan, Michael M. Bronstein, Pierre Vandergheynst, Adam Gosztolai

ICLR 2026 Efficient Learning on Large Graphs Using a Densifying Regularity Lemma Jonathan Kouchly, Ben Finkelshtein, Michael M. Bronstein, Ron Levie

ICLR 2026 Efficient Regression-Based Training of Normalizing Flows for Boltzmann Generators Danyal Rehman, Oscar Davis, Jiarui Lu, Jian Tang, Michael M. Bronstein, Yoshua Bengio, Alexander Tong, Joey Bose

ICLR 2026 Flock: A Knowledge Graph Foundation Model via Learning on Random Walks Jinwoo Kim, Xingyue Huang, Krzysztof Olejniczak, Kyungbin Min, Michael M. Bronstein, Seunghoon Hong, Ismail Ilkan Ceylan

ICLR 2026 Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds Oscar Davis, Michael Samuel Albergo, Nicholas Matthew Boffi, Michael M. Bronstein, Joey Bose

ICLR 2026 HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs Xingyue Huang, Mikhail Galkin, Michael M. Bronstein, Ismail Ilkan Ceylan

ICLR 2026 LLMs Can Hide Text in Other Text of the Same Length Antonio Norelli, Michael M. Bronstein

ICLR 2026 Learning Escorted Protocols for Multistate Free-Energy Estimation Lars Holdijk, Nithishwer Mouroug Anand, Michael M. Bronstein, Max Welling

ICLR 2026 MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Models Kacper Kapuśniak, Cristian Gabellini, Michael M. Bronstein, Prudencio Tossou, Francesco Di Giovanni

ICLR 2026 OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction Emily Jin, Andrei Cristian Nica, Mikhail Galkin, Jarrid Rector-Brooks, Kin Long Kelvin Lee, Santiago Miret, Frances H. Arnold, Michael M. Bronstein, Joey Bose, Alexander Tong, Cheng-Hao Liu

ICLR 2026 Planner Aware Path Learning in Diffusion Language Models Training Fred Zhangzhi Peng, Zachary Bezemek, Jarrid Rector-Brooks, Shuibai Zhang, Michael M. Bronstein, Anru Zhang, Joey Bose, Alexander Tong

ICLR 2026 ResCP: Reservoir Conformal Prediction for Time Series Forecasting Roberto Neglia, Andrea Cini, Michael M. Bronstein, Filippo Maria Bianchi

ICLR 2026 Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute Kieran Didi, Zuobai Zhang, Guoqing Zhou, Danny Reidenbach, Zhonglin Cao, Sooyoung Cha, Tomas Geffner, Christian Dallago, Jian Tang, Michael M. Bronstein, Martin Steinegger, Emine Kucukbenli, Arash Vahdat, Karsten Kreis

ICLR 2026 TGM: A Modular and Efficient Library for Machine Learning on Temporal Graphs Jacob Chmura, Shenyang Huang, Tran Gia Bao Ngo, Ali Parviz, Farimah Poursafaei, Jure Leskovec, Michael M. Bronstein, Guillaume Rabusseau, Matthias Fey, Reihaneh Rabbany

ICLR 2026 Towards Quantifying Long-Range Interactions in Graph Machine Learning: A Large Graph Dataset and a Measurement Huidong Liang, Haitz Sáez de Ocáriz Borde, Baskaran Sripathmanathan, Michael M. Bronstein, Xiaowen Dong

ICLR 2026 gLSTM: Mitigating Over-Squashing by Increasing Storage Capacity Hugh Blayney, Alvaro Arroyo, Xiaowen Dong, Michael M. Bronstein

ICML 2025 A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition Nian Liu, Xiaoxin He, Thomas Laurent, Francesco Di Giovanni, Michael M. Bronstein, Xavier Bresson

NeurIPS 2025 Amortized Sampling with Transferable Normalizing Flows Charlie B. Tan, Majdi Hassan, Leon Klein, Saifuddin Syed, Dominique Beaini, Michael M. Bronstein, Alexander Tong, Kirill Neklyudov

FTML 2025 Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Alex Strasser, Haiyang Yu, Yuqing Xie, Xiang Fu, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji

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

ICLR 2025 Bundle Neural Network for Message Diffusion on Graphs Jacob Bamberger, Federico Barbero, Xiaowen Dong, Michael M. Bronstein

NeurIPS 2025 Curly Flow Matching for Learning Non-Gradient Field Dynamics Katarina Petrović, Lazar Atanackovic, Viggo Moro, Kacper Kapuśniak, Ismail Ilkan Ceylan, Michael M. Bronstein, Joey Bose, Alexander Tong

ICLRW 2025 Curly Flow Matching for Learning Non-Gradient Field Dynamics Katarina Petrović, Lazar Atanackovic, Kacper Kapuśniak, Michael M. Bronstein, Joey Bose, Alexander Tong

ICLRW 2025 Curly Flow Matching for Learning Non-Gradient Field Dynamics Katarina Petrović, Lazar Atanackovic, Kacper Kapusniak, Michael M. Bronstein, Joey Bose, Alexander Tong

NeurIPS 2025 Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights Zhiyuan Liang, Dongwen Tang, Yuhao Zhou, Xuanlei Zhao, Mingjia Shi, Wangbo Zhao, Zekai Li, Peihao Wang, Konstantin Schürholt, Damian Borth, Michael M. Bronstein, Yang You, Zhangyang Wang, Kai Wang

TMLR 2025 DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models Zifeng Ding, Yifeng Li, Yuan He, Antonio Norelli, Jingcheng Wu, Volker Tresp, Michael M. Bronstein, Yunpu Ma

NeurIPS 2025 Equivariance Everywhere All at Once: A Recipe for Graph Foundation Models Ben Finkelshtein, Ismail Ilkan Ceylan, Michael M. Bronstein, Ron Levie

ICLRW 2025 Flow-Based Fragment Identification via Contrastive Learning of Binding Site-Specific Latent Representations Rebecca Manuela Neeser, Ilia Igashov, Arne Schneuing, Michael M. Bronstein, Philippe Schwaller, Bruno Correia

ICLRW 2025 Flows Don't Cross in High Dimension Teodora Reu, Sixtine Dromigny, Michael M. Bronstein, Francisco Vargas

ICLR 2025 Fully-Inductive Node Classification on Arbitrary Graphs Jianan Zhao, Zhaocheng Zhu, Mikhail Galkin, Hesham Mostafa, Michael M. Bronstein, Jian Tang

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

NeurIPS 2025 Gradient Variance Reveals Failure Modes in Flow-Based Generative Models Teodora Reu, Sixtine Dromigny, Michael M. Bronstein, Francisco Vargas

ICLRW 2025 Graph Low-Rank Adapters of High Regularity for Graph Neural Networks and Graph Transformers Pantelis Papageorgiou, Haitz Sáez de Ocáriz Borde, Anastasis Kratsios, Michael M. Bronstein

ICLRW 2025 Graph Networks Struggle with Variable Scale Christian Koke, Yuesong Shen, Abhishek Saroha, Marvin Eisenberger, Bastian Rieck, Michael M. Bronstein, Daniel Cremers

ICLR 2025 Homomorphism Counts as Structural Encodings for Graph Learning Linus Bao, Emily Jin, Michael M. Bronstein, Ismail Ilkan Ceylan, Matthias Lanzinger

ICML 2025 How Expressive Are Knowledge Graph Foundation Models? Xingyue Huang, Pablo Barcelo, Michael M. Bronstein, Ismail Ilkan Ceylan, Mikhail Galkin, Juan L Reutter, Miguel Romero Orth

ICLRW 2025 Large Drug Discovery Model Ilia Igashov, Arne Schneuing, Adrian W. Dobbelstein, Irina Morozova, Rebecca Manuela Neeser, Evgenia Elizarova, Philippe Schwaller, Michael M. Bronstein, Bruno Correia

TMLR 2025 Link Prediction with Relational Hypergraphs Xingyue Huang, Miguel Romero Orth, Pablo Barcelo, Michael M. Bronstein, Ismail Ilkan Ceylan

ICLR 2025 Multi-Domain Distribution Learning for De Novo Drug Design Arne Schneuing, Ilia Igashov, Adrian W. Dobbelstein, Thomas Castiglione, Michael M. Bronstein, Bruno Correia

ICLR 2025 Neural Spacetimes for DAG Representation Learning Haitz Sáez de Ocáriz Borde, Anastasis Kratsios, Marc T. Law, Xiaowen Dong, Michael M. Bronstein

ICLRW 2025 On Incorporating Scale into Graph Networks Christian Koke, Yuesong Shen, Abhishek Saroha, Marvin Eisenberger, Bastian Rieck, Michael M. Bronstein, Daniel Cremers

ICML 2025 On Measuring Long-Range Interactions in Graph Neural Networks Jacob Bamberger, Benjamin Gutteridge, Scott Le Roux, Michael M. Bronstein, Xiaowen Dong

ICLRW 2025 On Multi-Scale Graph Representation Learning Christian Koke, Dominik Schnaus, Yuesong Shen, Abhishek Saroha, Marvin Eisenberger, Bastian Rieck, Michael M. Bronstein, Daniel Cremers

NeurIPS 2025 On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning Alvaro Arroyo, Alessio Gravina, Benjamin Gutteridge, Federico Barbero, Claudio Gallicchio, Xiaowen Dong, Michael M. Bronstein, Pierre Vandergheynst

NeurIPS 2025 Over-Squashing in Spatiotemporal Graph Neural Networks Ivan Marisca, Jacob Bamberger, Cesare Alippi, Michael M. Bronstein

ICML 2025 Position: Graph Learning Will Lose Relevance Due to Poor Benchmarks Maya Bechler-Speicher, Ben Finkelshtein, Fabrizio Frasca, Luis Müller, Jan Tönshoff, Antoine Siraudin, Viktor Zaverkin, Michael M. Bronstein, Mathias Niepert, Bryan Perozzi, Mikhail Galkin, Christopher Morris

NeurIPS 2025 Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities Tara Akhound-Sadegh, Jungyoon Lee, Joey Bose, Valentin De Bortoli, Arnaud Doucet, Michael M. Bronstein, Dominique Beaini, Siamak Ravanbakhsh, Kirill Neklyudov, Alexander Tong

ICLRW 2025 Relaxed Equivariance via Multitask Learning Ahmed A. A. Elhag, T. Konstantin Rusch, Francesco Di Giovanni, Michael M. Bronstein

ICML 2025 Scalable Equilibrium Sampling with Sequential Boltzmann Generators Charlie B. Tan, Joey Bose, Chen Lin, Leon Klein, Michael M. Bronstein, Alexander Tong

ICLRW 2025 Scalable Equilibrium Sampling with Sequential Boltzmann Generators Charlie B. Tan, Joey Bose, Chen Lin, Leon Klein, Michael M. Bronstein, Alexander Tong

TMLR 2025 Setting the Record Straight on Transformer Oversmoothing Gbetondji Jean-Sebastien Dovonon, Michael M. Bronstein, Matt Kusner

ICLR 2025 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

ICML 2025 Supercharging Graph Transformers with Advective Diffusion Qitian Wu, Chenxiao Yang, Kaipeng Zeng, Michael M. Bronstein

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

ICLR 2025 Understanding Virtual Nodes: Oversquashing and Node Heterogeneity Joshua Southern, Francesco Di Giovanni, Michael M. Bronstein, Johannes F. Lutzeyer

ICMLW 2024 Bundle Neural Networks for Message Diffusion on Graphs Jacob Bamberger, Federico Barbero, Xiaowen Dong, Michael M. Bronstein

ICML 2024 Cooperative Graph Neural Networks Ben Finkelshtein, Xingyue Huang, Michael M. Bronstein, Ismail Ilkan Ceylan

NeurIPSW 2024 Enhancing the Expressivity of Temporal Graph Networks Through Source-Target Identification Benedict Aaron Tjandra, Federico Barbero, Michael M. Bronstein

ICLRW 2024 Expanding Genomic Discovery: Causally-Inspired Neural Networks for Predicting Therapeutic Targets Guadalupe Gonzalez, Isuru Herath, Kirill Veselkov, Michael M. Bronstein, Marinka Zitnik

ICLR 2024 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

NeurIPSW 2024 Fully-Inductive Node Classification on Arbitrary Graphs Jianan Zhao, Mikhail Galkin, Hesham Mostafa, Michael M. Bronstein, Zhaocheng Zhu, Jian Tang

NeurIPSW 2024 GraphText: Graph Reasoning in Text Space Jianan Zhao, Le Zhuo, Yikang Shen, Meng Qu, Kai Liu, Michael M. Bronstein, Zhaocheng Zhu, Jian Tang

NeurIPSW 2024 Homomorphism Counts as Structural Encodings for Molecular Property Prediction Linus Bao, Emily Jin, Michael M. Bronstein, Ismail Ilkan Ceylan, Matthias Lanzinger

ICML 2024 Homomorphism Counts for Graph Neural Networks: All About That Basis Emily Jin, Michael M. Bronstein, Ismail Ilkan Ceylan, Matthias Lanzinger

NeurIPSW 2024 Learning Protocols for Non-Equilibrium Conformational Free-Energy Estimation Using Optimal Transport and Conditional Flow Matching Lars Holdijk, Michael M. Bronstein, Max Welling

ICLR 2024 Locality-Aware Graph Rewiring in GNNs Federico Barbero, Ameya Velingker, Amin Saberi, Michael M. Bronstein, Francesco Di Giovanni

ICMLW 2024 Message-Passing Monte Carlo: Generating Low-Discrepancy Point Sets via Graph Neural Networks T. Konstantin Rusch, Nathan Kirk, Michael M. Bronstein, Christiane Lemieux, Daniela Rus

ICMLW 2024 On the Effectiveness of Quantum Chemistry Pre-Training for Pharmacological Property Prediction Arun Raja, Hongtao Zhao, Christian Tyrchan, Eva Nittinger, Michael M. Bronstein, Charlotte Deane, Garrett M Morris

NeurIPS 2024 On the Limitations of Fractal Dimension as a Measure of Generalization Charlie B. Tan, Inés García-Redondo, Qiquan Wang, Michael M. Bronstein, Anthea Monod

ICMLW 2024 PLINDER: The Protein-Ligand Interactions Dataset and Evaluation Resource Janani Durairaj, Yusuf Adeshina, Zhonglin Cao, Xuejin Zhang, Vladas Oleinikovas, Thomas Duignan, Zachary McClure, Xavier Robin, Emanuele Rossi, Guoqing Zhou, Srimukh Prasad Veccham, Clemens Isert, Yuxing Peng, Prabindh Sundareson, Mehmet Akdel, Gabriele Corso, Hannes Stark, Zachary Wayne Carpenter, Michael M. Bronstein, Emine Kucukbenli, Torsten Schwede, Luca Naef

ICML 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

ICML 2024 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 Veličković, Bei Wang, Yusu Wang, Guowei Wei, Ghada Zamzmi

ICLR 2024 RetroBridge: Modeling Retrosynthesis with Markov Bridges Ilia Igashov, Arne Schneuing, Marwin Segler, Michael M. Bronstein, Bruno Correia

ICLR 2024 SE(3)-Stochastic Flow Matching for Protein Backbone Generation 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

ICLRW 2024 Setting the Record Straight on Transformer Oversmoothing Gbetondji Jean-Sebastien Dovonon, Michael M. Bronstein, Matt Kusner

ICMLW 2024 Setting the Record Straight on Transformer Oversmoothing Gbetondji Jean-Sebastien Dovonon, Michael M. Bronstein, Matt Kusner

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

ICLRW 2024 Towards Structure-Based Drug Design with Protein Flexibility Arne Schneuing, Ilia Igashov, Thomas Castiglione, Michael M. Bronstein, Bruno Correia

ICMLW 2024 Transferability for Graph Convolutional Networks Christian Koke, Abhishek Saroha, Yuesong Shen, Marvin Eisenberger, Michael M. Bronstein, Daniel Cremers

ICMLW 2023 Can Strong Structural Encoding Reduce the Importance of Message Passing? Floor Eijkelboom, Erik J Bekkers, Michael M. Bronstein, Francesco Di Giovanni

ICML 2023 DRew: Dynamically Rewired Message Passing with Delay Benjamin Gutteridge, Xiaowen Dong, Michael M. Bronstein, Francesco Di Giovanni

LoG 2023 Edge Directionality Improves Learning on Heterophilic Graphs Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein

ICMLW 2023 Evaluation Metrics for Protein Structure Generation Joshua Southern, Arne Schneuing, Michael M. Bronstein, Bruno Correia

ICLRW 2023 Flexible Small-Molecule Design and Optimization with Equivariant Diffusion Models Charles Harris, Kieran Didi, Arne Schneuing, Yuanqi Du, Arian Rokkum Jamasb, Michael M. Bronstein, Bruno Correia, Pietro Lio, Tom Leon Blundell

ICLR 2023 Gradient Gating for Deep Multi-Rate Learning on Graphs T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra

ICLR 2023 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

ICLR 2023 Hyperbolic Deep Reinforcement Learning Edoardo Cetin, Benjamin Paul Chamberlain, Michael M. Bronstein, Jonathan J Hunt

ICML 2023 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

ICMLW 2023 On the Expressive Power of Ollivier-Ricci Curvature on Graphs Joshua Southern, Jeremy Wayland, Michael M. Bronstein, Bastian Rieck

AAAI 2023 Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderón, Michael M. Bronstein, Marcello Restelli

NeurIPSW 2023 RetroBridge: Modeling Retrosynthesis with Markov Bridges Ilia Igashov, Arne Schneuing, Marwin Segler, Michael M. Bronstein, Bruno Correia

TMLR 2023 Understanding Convolution on Graphs via Energies Francesco Di Giovanni, James Rowbottom, Benjamin Paul Chamberlain, Thomas Markovich, Michael M. Bronstein

ICLRW 2022 Decoding Surface Fingerprints for Protein-Ligand Interactions Ilia Igashov, Arian Rokkum Jamasb, Ahmed Sadek, Freyr Sverrisson, Arne Schneuing, Tom Blundell, Pietro Lio, Michael M. Bronstein, Bruno Correia

NeurIPSW 2022 Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design Ilia Igashov, Hannes Stärk, Clement Vignac, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael M. Bronstein, Bruno Correia

ICLR 2022 Equivariant Subgraph Aggregation Networks Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron

ICLRW 2022 Evaluating Generalization in GFlowNets for Molecule Design Andrei Cristian Nica, Moksh Jain, Emmanuel Bengio, Cheng-Hao Liu, Maksym Korablyov, Michael M. Bronstein, Yoshua Bengio

ICLRW 2022 Graph Anisotropic Diffusion Ahmed A. A. Elhag, Gabriele Corso, Hannes Stärk, Michael M. Bronstein

ICLRW 2022 Graph Anisotropic Diffusion for Molecules Ahmed A. A. Elhag, Gabriele Corso, Hannes Stärk, Michael M. Bronstein

ICLRW 2022 Heterogeneous Manifolds for Curvature-Aware Graph Embedding Francesco Di Giovanni, Giulia Luise, Michael M. Bronstein

NeurIPSW 2022 Hyperbolic Deep Reinforcement Learning Edoardo Cetin, Benjamin Paul Chamberlain, Michael M. Bronstein, Jonathan J Hunt

ICMLW 2022 Invariance Discovery for Systematic Generalization in Reinforcement Learning Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderon, Michael M. Bronstein, Marcello Restelli

ICLRW 2022 Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Lio, Michael M. Bronstein

LoG 2022 On the Unreasonable Effectiveness of Feature Propagation in Learning on Graphs with Missing Node Features Emanuele Rossi, Henry Kenlay, Maria I. Gorinova, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein

NeurIPSW 2022 On the Unreasonable Effectiveness of Feature Propagation in Learning on Graphs with Missing Node Features Emanuele Rossi, Henry Kenlay, Maria I. Gorinova, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein

ICLRW 2022 Physics-Informed Deep Neural Network for Rigid-Body Protein Docking Freyr Sverrisson, Jean Feydy, Joshua Southern, Michael M. Bronstein, Bruno Correia

NeurIPSW 2022 Provably Efficient Causal Model-Based Reinforcement Learning for Environment-Agnostic Generalization Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderon, Michael M. Bronstein, Marcello Restelli

ICLR 2022 Understanding Over-Squashing and Bottlenecks on Graphs via Curvature Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein

CVPR 2021 Fast End-to-End Learning on Protein Surfaces Freyr Sverrisson, Jean Feydy, Bruno E. Correia, Michael M. Bronstein

NeurIPSW 2021 GRAND: Graph Neural Diffusion Benjamin Paul Chamberlain, James Rowbottom, Maria I. Gorinova, Stefan D Webb, Emanuele Rossi, Michael M. Bronstein

ICLRW 2021 Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks Cristian Bodnar, Fabrizio Frasca, Yu Guang Wang, Nina Otter, Guido Montufar, Pietro Liò, Michael M. Bronstein

CVPR 2020 Geometrically Principled Connections in Graph Neural Networks Shunwang Gong, Mehdi Bahri, Michael M. Bronstein, Stefanos Zafeiriou

CVPR 2020 Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild Dominik Kulon, Riza Alp Guler, Iasonas Kokkinos, Michael M. Bronstein, Stefanos Zafeiriou

CVPR 2019 GFrames: Gradient-Based Local Reference Frame for 3D Shape Matching Simone Melzi, Riccardo Spezialetti, Federico Tombari, Michael M. Bronstein, Luigi Di Stefano, Emanuele Rodola

CVPR 2019 Isospectralization, or How to Hear Shape, Style, and Correspondence Luca Cosmo, Mikhail Panine, Arianna Rampini, Maks Ovsjanikov, Michael M. Bronstein, Emanuele Rodola

ICCVW 2019 SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator Shunwang Gong, Lei Chen, Michael M. Bronstein, Stefanos Zafeiriou

CVPR 2017 Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, Michael M. Bronstein

CVPR 2016 Efficient Globally Optimal 2D-to-3D Deformable Shape Matching Zorah Lahner, Emanuele Rodola, Frank R. Schmidt, Michael M. Bronstein, Daniel Cremers

ECCV 2016 MADMM: A Generic Algorithm for Non-Smooth Optimization on Manifolds Artiom Kovnatsky, Klaus Glashoff, Michael M. Bronstein

CVPR 2015 Functional Correspondence by Matrix Completion Artiom Kovnatsky, Michael M. Bronstein, Xavier Bresson, Pierre Vandergheynst

ICCVW 2015 Geodesic Convolutional Neural Networks on Riemannian Manifolds Jonathan Masci, Davide Boscaini, Michael M. Bronstein, Pierre Vandergheynst

ICLR 2014 Sparse Similarity-Preserving Hashing Jonathan Masci, Alexander M. Bronstein, Michael M. Bronstein, Pablo Sprechmann, Guillermo Sapiro

ECCV 2012 Group-Valued Regularization for Analysis of Articulated Motion Guy Rosman, Alexander M. Bronstein, Michael M. Bronstein, Xue-Cheng Tai, Ron Kimmel

ECCVW 2012 Group-Valued Regularization for Analysis of Articulated Motion Guy Rosman, Alexander M. Bronstein, Michael M. Bronstein, Xue-Cheng Tai, Ron Kimmel

CVPR 2012 Intrinsic Shape Context Descriptors for Deformable Shapes Iasonas Kokkinos, Michael M. Bronstein, Roee Litman, Alexander M. Bronstein

ECCV 2012 Putting the Pieces Together: Regularized Multi-Part Shape Matching Or Litany, Alexander M. Bronstein, Michael M. Bronstein

ECCVW 2012 Putting the Pieces Together: Regularized Multi-Part Shape Matching Or Litany, Alexander M. Bronstein, Michael M. Bronstein

ECCV 2012 Stable Spectral Mesh Filtering Artiom Kovnatsky, Michael M. Bronstein, Alexander M. Bronstein

ECCVW 2012 Stable Spectral Mesh Filtering Artiom Kovnatsky, Michael M. Bronstein, Alexander M. Bronstein

CVPR 2011 Affine-Invariant Diffusion Geometry for the Analysis of Deformable 3D Shapes Dan Raviv, Michael M. Bronstein, Alexander M. Bronstein, Ron Kimmel, Nir A. Sochen

CVPR 2010 Data Fusion Through Cross-Modality Metric Learning Using Similarity-Sensitive Hashing Michael M. Bronstein, Alexander M. Bronstein, Fabrice Michel, Nikos Paragios

ECCV 2010 Intrinsic Regularity Detection in 3D Geometry Niloy J. Mitra, Alexander M. Bronstein, Michael M. Bronstein

CVPR 2010 Scale-Invariant Heat Kernel Signatures for Non-Rigid Shape Recognition Michael M. Bronstein, Iasonas Kokkinos

ECCV 2010 Spatially-Sensitive Affine-Invariant Image Descriptors Alexander M. Bronstein, Michael M. Bronstein

ICCVW 2009 3D-Color Video Camera O. Rubinstein, Yaron Honen, Alexander M. Bronstein, Michael M. Bronstein, Ron Kimmel

ICCVW 2009 On Reconstruction of Non-Rigid Shapes with Intrinsic Regularization Yohai S. Devir, Guy Rosman, Alexander M. Bronstein, Michael M. Bronstein, Ron Kimmel

ICCVW 2009 Shape Google: A Computer Vision Approach to Isometry Invariant Shape Retrieval Maks Ovsjanikov, Alexander M. Bronstein, Michael M. Bronstein, Leonidas J. Guibas

CVPRW 2008 Not Only Size Matters: Regularized Partial Matching of Nonrigid Shapes Alexander M. Bronstein, Michael M. Bronstein

ECCV 2008 Regularized Partial Matching of Rigid Shapes Alexander M. Bronstein, Michael M. Bronstein

ICCV 2007 Rock, Paper, and Scissors: Extrinsic vs. Intrinsic Similarity of Non-Rigid Shapes Alexander M. Bronstein, Michael M. Bronstein, Ron Kimmel

ICCV 2007 Symmetries of Non-Rigid Shapes Dan Raviv, Alexander M. Bronstein, Michael M. Bronstein, Ron Kimmel

ECCV 2006 Robust Expression-Invariant Face Recognition from Partially Missing Data Alexander M. Bronstein, Michael M. Bronstein, Ron Kimmel

ECCV 2004 Face Recognition from Facial Surface Metric Alexander M. Bronstein, Michael M. Bronstein, Alon Spira, Ron Kimmel