Michael M. Bronstein

ORCID

Publication Activity (10 Years)

Years Active: 2002-2024
Publications (10 Years): 213

Top Venues

Publications

Oscar Davis, Samuel Kessler, Mircea Petrache, Ismail Ilkan Ceylan, Michael M. Bronstein, Avishek Joey Bose
Fisher Flow Matching for Generative Modeling over Discrete Data. CoRR (2024)
Nian Liu, Xiaoxin He, Thomas Laurent, Francesco Di Giovanni, Michael M. Bronstein, Xavier Bresson
Advancing Graph Convolutional Networks via General Spectral Wavelets. CoRR (2024)
Ilia Igashov, Arne Schneuing, Marwin H. S. Segler, Michael M. Bronstein, Bruno E. Correia
RetroBridge: Modeling Retrosynthesis with Markov Bridges. ICLR (2024)
Jianan Zhao, Hesham Mostafa, Mikhail Galkin, Michael M. Bronstein, Zhaocheng Zhu, Jian Tang
GraphAny: A Foundation Model for Node Classification on Any Graph. CoRR (2024)
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
The 1st International Workshop on Graph Foundation Models (GFM). WWW (Companion Volume) (2024)
T. Konstantin Rusch, Nathan Kirk, Michael M. Bronstein, Christiane Lemieux, Daniela Rus
Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks. CoRR (2024)
Baskaran Sripathmanathan, Xiaowen Dong, Michael M. Bronstein
On the Impact of Sample Size in Reconstructing Noisy Graph Signals: A Theoretical Characterisation. CoRR (2024)
Gbètondji J.-S. Dovonon, Michael M. Bronstein, Matt J. Kusner
Setting the Record Straight on Transformer Oversmoothing. CoRR (2024)
Jacob Bamberger, Federico Barbero, Xiaowen Dong, Michael M. Bronstein
Bundle Neural Networks for message diffusion on graphs. CoRR (2024)
Xingyue Huang, Miguel A. Romero Orth, Pablo Barceló, Michael M. Bronstein, Ismail Ilkan Ceylan
Link Prediction with Relational Hypergraphs. CoRR (2024)
Federico Barbero, Ameya Velingker, Amin Saberi, Michael M. Bronstein, Francesco Di Giovanni
Locality-Aware Graph Rewiring in GNNs. ICLR (2024)
Ben Finkelshtein, Ismail Ilkan Ceylan, Michael M. Bronstein, Ron Levie
Learning on Large Graphs using Intersecting Communities. CoRR (2024)
Charlie Tan, Inés García-Redondo, Qiquan Wang, Michael M. Bronstein, Anthea Monod
On the Limitations of Fractal Dimension as a Measure of Generalization. CoRR (2024)
Kacper Kapusniak, Peter Potaptchik, Teodora Reu, Leo Zhang, Alexander Tong, Michael M. Bronstein, Avishek Joey Bose, Francesco Di Giovanni
Metric Flow Matching for Smooth Interpolations on the Data Manifold. CoRR (2024)
Claudio Battiloro, Indro Spinelli, Lev Telyatnikov, Michael M. Bronstein, Simone Scardapane, Paolo Di Lorenzo
From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module. ICLR (2024)
Maksym Korablyov, Cheng-Hao Liu, Moksh Jain, Almer M. van der Sloot, Eric Jolicoeur, Edward Ruediger, Andrei Cristian Nica, Emmanuel Bengio, Kostiantyn Lapchevskyi, Daniel St-Cyr, Doris Alexandra Schuetz, Victor Ion Butoi, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, Sai Krishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampásek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin H. S. Segler, Michael M. Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio
Generative Active Learning for the Search of Small-molecule Protein Binders. CoRR (2024)
Ilia Igashov, Hannes Stärk, Clément Vignac, Arne Schneuing, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael M. Bronstein, Bruno E. Correia
Equivariant 3D-conditional diffusion model for molecular linker design. Nat. Mac. Intell. 6 (4) (2024)
Chen Lin, Liheng Ma, Yiyang Chen, Wanli Ouyang, Michael M. Bronstein, Philip H. S. Torr
Revealing Decurve Flows for Generalized Graph Propagation. CoRR (2024)
Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Velickovic
How does over-squashing affect the power of GNNs? Trans. Mach. Learn. Res. 2024 (2024)
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
SE(3)-Stochastic Flow Matching for Protein Backbone Generation. ICLR (2024)
Guillaume Huguet, James Vuckovic, Kilian Fatras, Eric Thibodeau-Laufer, Pablo Lemos, Riashat Islam, Cheng-Hao Liu, Jarrid Rector-Brooks, Tara Akhound-Sadegh, Michael M. Bronstein, Alexander Tong, Avishek Joey Bose
Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation. CoRR (2024)
Joshua Southern, Francesco Di Giovanni, Michael M. Bronstein, Johannes F. Lutzeyer
Understanding Virtual Nodes: Oversmoothing, Oversquashing, and Node Heterogeneity. CoRR (2024)
Baskaran Sripathmanathan, Xiaowen Dong, Michael M. Bronstein
On the Impact of Sample Size in Reconstructing Graph Signals. CoRR (2023)
Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Yannick Hammerla, Michael M. Bronstein, Max Hansmire
Graph Neural Networks for Link Prediction with Subgraph Sketching. ICLR (2023)
Floor Eijkelboom, Erik J. Bekkers, Michael M. Bronstein, Francesco Di Giovanni
Can strong structural encoding reduce the importance of Message Passing? TAG-ML (2023)
Benjamin Gutteridge, Xiaowen Dong, Michael M. Bronstein, Francesco Di Giovanni
DRew: Dynamically Rewired Message Passing with Delay. ICML (2023)
Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderón, Michael M. Bronstein, Marcello Restelli
Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization. AAAI (2023)
Federico Barbero, Ameya Velingker, Amin Saberi, Michael M. Bronstein, Francesco Di Giovanni
Locality-Aware Graph-Rewiring in GNNs. CoRR (2023)
Joshua Southern, Jeremy Wayland, Michael M. Bronstein, Bastian Rieck
Curvature Filtrations for Graph Generative Model Evaluation. CoRR (2023)
Edoardo Cetin, Benjamin Paul Chamberlain, Michael M. Bronstein, Jonathan J. Hunt
Hyperbolic Deep Reinforcement Learning. ICLR (2023)
Kamilia Zaripova, Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Michael M. Bronstein, Nassir Navab
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications. Medical Image Anal. 88 (2023)
Floor Eijkelboom, Erik J. Bekkers, Michael M. Bronstein, Francesco Di Giovanni
Can strong structural encoding reduce the importance of Message Passing? CoRR (2023)
Anees Kazi, Luca Cosmo, Seyed-Ahmad Ahmadi, Nassir Navab, Michael M. Bronstein
Differentiable Graph Module (DGM) for Graph Convolutional Networks. IEEE Trans. Pattern Anal. Mach. Intell. 45 (2) (2023)
Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio, Michael M. Bronstein
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology. ICML (2023)
T. Konstantin Rusch, Michael M. Bronstein, Siddhartha Mishra
A Survey on Oversmoothing in Graph Neural Networks. CoRR (2023)
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, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, 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 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
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR (2023)
Jianan Zhao, Le Zhuo, Yikang Shen, Meng Qu, Kai Liu, Michael M. Bronstein, Zhaocheng Zhu, Jian Tang
GraphText: Graph Reasoning in Text Space. CoRR (2023)
Benjamin Gutteridge, Xiaowen Dong, Michael M. Bronstein, Francesco Di Giovanni
DRew: Dynamically Rewired Message Passing with Delay. CoRR (2023)
Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Lio, Yoshua Bengio, Michael M. Bronstein
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems. CoRR (2023)
Avishek Joey Bose, Tara Akhound-Sadegh, Kilian Fatras, Guillaume Huguet, Jarrid Rector-Brooks, Cheng-Hao Liu, Andrei Cristian Nica, Maksym Korablyov, Michael M. Bronstein, Alexander Tong
SE(3)-Stochastic Flow Matching for Protein Backbone Generation. CoRR (2023)
Francesco Di Giovanni, James Rowbottom, Benjamin Paul Chamberlain, Thomas Markovich, Michael M. Bronstein
Understanding convolution on graphs via energies. Trans. Mach. Learn. Res. 2023 (2023)
Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein
Edge Directionality Improves Learning on Heterophilic Graphs. CoRR (2023)
T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra
Gradient Gating for Deep Multi-Rate Learning on Graphs. ICLR (2023)
Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio', Michael M. Bronstein
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology. CoRR (2023)
Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, Michael M. Bronstein
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting. IEEE Trans. Pattern Anal. Mach. Intell. 45 (1) (2023)
Ilia Igashov, Arne Schneuing, Marwin Segler, Michael M. Bronstein, Bruno E. Correia
RetroBridge: Modeling Retrosynthesis with Markov Bridges. CoRR (2023)
António Leitão, Maxime Lucas, Simone Poetto, Taylor A. Hersh, Shane Gero, David F. Gruber, Michael M. Bronstein, Giovanni Petri
Social learning across symbolic cultural barriers in non-human cultures. CoRR (2023)
Claudio Battiloro, Indro Spinelli, Lev Telyatnikov, Michael M. Bronstein, Simone Scardapane, Paolo Di Lorenzo
From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module. CoRR (2023)
Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein
Edge Directionality Improves Learning on Heterophilic Graphs. LoG (2023)
Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Velickovic
How does over-squashing affect the power of GNNs? CoRR (2023)
Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael M. Bronstein, Guillaume Rabusseau, Reihaneh Rabbany
Temporal Graph Benchmark for Machine Learning on Temporal Graphs. CoRR (2023)
Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael M. Bronstein, Guillaume Rabusseau, Reihaneh Rabbany
Temporal Graph Benchmark for Machine Learning on Temporal Graphs. NeurIPS (2023)
Emanuele Rossi, Federico Monti, Yan Leng, Michael M. Bronstein, Xiaowen Dong
Learning to Infer Structures of Network Games. ICML (2022)
Arne Schneuing, Yuanqi Du, Charles Harris, Arian R. Jamasb, Ilia Igashov, Weitao Du, Tom L. Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael M. Bronstein, Bruno E. Correia
Structure-based Drug Design with Equivariant Diffusion Models. CoRR (2022)
Ahmed El-Kishky, Michael M. Bronstein, Ying Xiao, Aria Haghighi
Graph-based Representation Learning for Web-scale Recommender Systems. KDD (2022)
Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Lió, Michael M. Bronstein
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs. NeurIPS (2022)
Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron
Equivariant Subgraph Aggregation Networks. ICLR (2022)
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein
Understanding over-squashing and bottlenecks on graphs via curvature. ICLR (2022)
T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra
Gradient Gating for Deep Multi-Rate Learning on Graphs. CoRR (2022)
Soha Sadat Mahdi, Harold Matthews, Nele Nauwelaers, Michiel Vanneste, Shunwang Gong, Giorgos Bouritsas, Gareth S. Baynam, Peter Hammond, Richard A. Spritz, Ophir D. Klein, Benedikt Hallgrímsson, Hilde Peeters, Michael M. Bronstein, Peter Claes
Multi-Scale Part-Based Syndrome Classification of 3D Facial Images. IEEE Access 10 (2022)
Ahmed A. A. Elhag, Gabriele Corso, Hannes Stärk, Michael M. Bronstein
Graph Anisotropic Diffusion. CoRR (2022)
Kamilia Mullakaeva, Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, Michael M. Bronstein
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications. CoRR (2022)
Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael M. Bronstein, Petar Velickovic, Pietro Liò
Sheaf Neural Networks with Connection Laplacians. CoRR (2022)
Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Liò, Michael M. Bronstein
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs. CoRR (2022)
Edoardo Cetin, Benjamin Paul Chamberlain, Michael M. Bronstein, Jonathan J. Hunt
Hyperbolic Deep Reinforcement Learning. CoRR (2022)
T. Konstantin Rusch, Benjamin Paul Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein
Graph-Coupled Oscillator Networks. CoRR (2022)
T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein
Graph-Coupled Oscillator Networks. ICML (2022)
Emanuele Rossi, Henry Kenlay, Maria I. Gorinova, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein
On the Unreasonable Effectiveness of Feature Propagation in Learning on Graphs With Missing Node Features. LoG (2022)
Francesco Di Giovanni, James Rowbottom, Benjamin Paul Chamberlain, Thomas Markovich, Michael M. Bronstein
Graph Neural Networks as Gradient Flows. CoRR (2022)
Fabrizio Frasca, Beatrice Bevilacqua, Michael M. Bronstein, Haggai Maron
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries. NeurIPS (2022)
Stefanos Zafeiriou, Michael M. Bronstein, Taco Cohen, Oriol Vinyals, Le Song, Jure Leskovec, Pietro Liò, Joan Bruna, Marco Gori
Guest Editorial: Non-Euclidean Machine Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44 (2) (2022)
Emanuele Rossi, Federico Monti, Yan Leng, Michael M. Bronstein, Xiaowen Dong
Learning to Infer Structures of Network Games. CoRR (2022)
Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael M. Bronstein, Petar Velickovic, Pietro Liò
Sheaf Neural Networks with Connection Laplacians. TAG-ML (2022)
Luca Cosmo, Giorgia Minello, Michael M. Bronstein, Emanuele Rodolà, Luca Rossi, Andrea Torsello
3D Shape Analysis Through a Quantum Lens: the Average Mixing Kernel Signature. Int. J. Comput. Vis. 130 (6) (2022)
Soha Sadat Mahdi, Nele Nauwelaers, Philip Joris, Giorgos Bouritsas, Shunwang Gong, Susan Walsh, Mark D. Shriver, Michael M. Bronstein, Peter Claes
Matching 3D Facial Shape to Demographic Properties by Geometric Metric Learning: A Part-Based Approach. IEEE Trans. Biom. Behav. Identity Sci. 4 (2) (2022)
Paul Bertin, Jarrid Rector-Brooks, Deepak Sharma, Thomas Gaudelet, Andrew Anighoro, Torsten Gross, Francisco Martinez-Pena, Eileen L. Tang, Suraj M. S, Cristian Regep, Jeremy B. R. Hayter, Maksym Korablyov, Nicholas Valiante, Almer van der Sloot, Mike Tyers, Charles Roberts, Michael M. Bronstein, Luke L. Lairson, Jake P. Taylor-King, Yoshua Bengio
RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds in vitro. CoRR (2022)
Francesco Di Giovanni, Giulia Luise, Michael M. Bronstein
Heterogeneous manifolds for curvature-aware graph embedding. CoRR (2022)
Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Hammerla, Michael M. Bronstein, Max Hansmire
Graph Neural Networks for Link Prediction with Subgraph Sketching. CoRR (2022)
Emanuele Rodolà, Luca Cosmo, Maks Ovsjanikov, Arianna Rampini, Simone Melzi, Michael M. Bronstein, Riccardo Marin
Inverse Computational Spectral Geometry. Eurographics (Tutorials) (2022)
Michael M. Bronstein
Neural Diffusion PDEs, Differential Geometry, and Graph Neural Networks. IMPROVE (2022)
Ilia Igashov, Hannes Stärk, Clément Vignac, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael M. Bronstein, Bruno E. Correia
Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design. CoRR (2022)
Fabrizio Frasca, Beatrice Bevilacqua, Michael M. Bronstein, Haggai Maron
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries. CoRR (2022)
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein
Understanding over-squashing and bottlenecks on graphs via curvature. CoRR (2021)
Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Velickovic
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. CoRR (2021)
Ben Chamberlain, James Rowbottom, Maria I. Gorinova, Michael M. Bronstein, Stefan Webb, Emanuele Rossi
GRAND: Graph Neural Diffusion. ICML (2021)
Thibaut Thonet, Stéphane Clinchant, Carlos Lassance, Elvin Isufi, Jiaqi Ma, Yutong Xie, Jean-Michel Renders, Michael M. Bronstein
GReS: Workshop on Graph Neural Networks for Recommendation and Search. RecSys (2021)
Benjamin Paul Chamberlain, James Rowbottom, Maria I. Gorinova, Stefan Webb, Emanuele Rossi, Michael M. Bronstein
GRAND: Graph Neural Diffusion. CoRR (2021)
Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yuguang Wang, Pietro Liò, Guido F. Montúfar, Michael M. Bronstein
Weisfeiler and Lehman Go Cellular: CW Networks. NeurIPS (2021)
Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yu Guang Wang, Pietro Liò, Guido Montúfar, Michael M. Bronstein
Weisfeiler and Lehman Go Cellular: CW Networks. CoRR (2021)
Benjamin Paul Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, Michael M. Bronstein
Beltrami Flow and Neural Diffusion on Graphs. CoRR (2021)
Emanuele Rossi, Henry Kenlay, Maria I. Gorinova, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein
On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features. CoRR (2021)
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
The 2021 RecSys Challenge Dataset: Fairness is not optional. CoRR (2021)
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
RecSys 2021 Challenge Workshop: Fairness-aware engagement prediction at scale on Twitter's Home Timeline. RecSys (2021)
Jacob Andreas, Gasper Begus, Michael M. Bronstein, Roee Diamant, Denley Delaney, Shane Gero, Shafi Goldwasser, David F. Gruber, Sarah de Haas, Peter Malkin, Roger Payne, Giovanni Petri, Daniela Rus, Pratyusha Sharma, Dan Tchernov, Pernille Tønnesen, Antonio Torralba, Daniel M. Vogt, Robert J. Wood
Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales. CoRR (2021)
Ben Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, Michael M. Bronstein
Beltrami Flow and Neural Diffusion on Graphs. NeurIPS (2021)
Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron
Equivariant Subgraph Aggregation Networks. CoRR (2021)
Luca Cosmo, Giorgia Minello, Michael M. Bronstein, Emanuele Rodolà, Luca Rossi, Andrea Torsello
Graph Kernel Neural Networks. CoRR (2021)
Stefan C. Schonsheck, Michael M. Bronstein, Rongjie Lai
Nonisometric Surface Registration via Conformal Laplace-Beltrami Basis Pursuit. J. Sci. Comput. 86 (3) (2021)
Cristian Bodnar, Fabrizio Frasca, Yu Guang Wang, Nina Otter, Guido Montúfar, Pietro Liò, Michael M. Bronstein
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. CoRR (2021)
Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F. Montúfar, Pietro Lió, Michael M. Bronstein
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. ICML (2021)

Michael M. Bronstein.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Michael M. Bronstein