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Benjamin Paul Chamberlain
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- affiliation (PhD 2018): Imperial College London, UK
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2020 – today
- 2023
- [j2]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) - [c23]Edoardo Cetin, Benjamin Paul Chamberlain, Michael M. Bronstein, Jonathan J. Hunt:
Hyperbolic Deep Reinforcement Learning. ICLR 2023 - [c22]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 - [c21]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 - [c20]Soledad Villar, Benjamin Paul Chamberlain, Yuanqi Du, Hannes Stärk, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Yanqiao Zhu, Kexin Huang, Michelle M. Li, Sofia Bourhim, Ilia Igashov, Alexandre Duval, Mathieu Alain, Dominique Beaini, Xinyu Yuan:
The Second Learning on Graphs Conference: Preface. LoG 2023: i-xix - [e1]Soledad Villar, Benjamin Chamberlain:
Learning on Graphs Conference, 27-30 November 2023, Virtual Event. Proceedings of Machine Learning Research 231, PMLR 2023 [contents] - 2022
- [c19]Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein:
Understanding over-squashing and bottlenecks on graphs via curvature. ICLR 2022 - [c18]T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein:
Graph-Coupled Oscillator Networks. ICML 2022: 18888-18909 - [c17]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: 11 - [c16]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 - [i26]T. Konstantin Rusch, Benjamin Paul Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein:
Graph-Coupled Oscillator Networks. CoRR abs/2202.02296 (2022) - [i25]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 abs/2202.04579 (2022) - [i24]Francesco Di Giovanni, James Rowbottom, Benjamin Paul Chamberlain, Thomas Markovich, Michael M. Bronstein:
Graph Neural Networks as Gradient Flows. CoRR abs/2206.10991 (2022) - [i23]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 abs/2209.15486 (2022) - [i22]T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra:
Gradient Gating for Deep Multi-Rate Learning on Graphs. CoRR abs/2210.00513 (2022) - [i21]Edoardo Cetin, Benjamin Paul Chamberlain, Michael M. Bronstein, Jonathan J. Hunt:
Hyperbolic Deep Reinforcement Learning. CoRR abs/2210.01542 (2022) - 2021
- [c15]Ben Chamberlain, James Rowbottom, Maria I. Gorinova, Michael M. Bronstein, Stefan Webb, Emanuele Rossi:
GRAND: Graph Neural Diffusion. ICML 2021: 1407-1418 - [c14]Ben Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, Michael M. Bronstein:
Beltrami Flow and Neural Diffusion on Graphs. NeurIPS 2021: 1594-1609 - [c13]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. RecSys Challenge 2021: 1-6 - [c12]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: 819-824 - [i20]Benjamin Paul Chamberlain, James Rowbottom, Maria I. Gorinova, Stefan Webb, Emanuele Rossi, Michael M. Bronstein:
GRAND: Graph Neural Diffusion. CoRR abs/2106.10934 (2021) - [i19]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 abs/2109.08245 (2021) - [i18]Benjamin Paul Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, Michael M. Bronstein:
Beltrami Flow and Neural Diffusion on Graphs. CoRR abs/2110.09443 (2021) - [i17]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 abs/2111.12128 (2021) - [i16]Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein:
Understanding over-squashing and bottlenecks on graphs via curvature. CoRR abs/2111.14522 (2021) - 2020
- [j1]C. H. Bryan Liu
, Benjamin Paul Chamberlain, Emma J. McCoy
What is the Value of Experimentation and Measurement? Data Sci. Eng. 5(2): 152-167 (2020) - [c11]Elaine M. Bettaney, Stephen R. Hardwick, Odysseas Zisimopoulos, Benjamin Paul Chamberlain:
Fashion Outfit Generation for E-Commerce. ECML/PKDD (5) 2020: 339-354 - [c10]Benjamin Paul Chamberlain, Emanuele Rossi, Dan Shiebler, Suvash Sedhain, Michael M. Bronstein:
Tuning Word2vec for Large Scale Recommendation Systems. RecSys 2020: 732-737 - [i15]Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, Davide Eynard, Michael M. Bronstein, Federico Monti:
SIGN: Scalable Inception Graph Neural Networks. CoRR abs/2004.11198 (2020) - [i14]Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, Michael M. Bronstein:
Temporal Graph Networks for Deep Learning on Dynamic Graphs. CoRR abs/2006.10637 (2020) - [i13]Benjamin Paul Chamberlain, Emanuele Rossi, Dan Shiebler, Suvash Sedhain, Michael M. Bronstein:
Tuning Word2vec for Large Scale Recommendation Systems. CoRR abs/2009.12192 (2020)
2010 – 2019
- 2019
- [c9]C. H. Bryan Liu, Benjamin Paul Chamberlain:
What is the Value of Experimentation & Measurement? ICDM 2019: 1222-1227 - [c8]Elaine M. Bettaney, Stephen R. Hardwick, Benjamin Paul Chamberlain, Odysseas Zisimopoulos:
Fashion Outfit Generation for E-commerce. eCOM@SIGIR 2019 - [c7]Kallirroi Dogani, Matteo Tomassetti, Saúl Vargas, Benjamin Paul Chamberlain, Sofie De Cnudde:
Learning Embeddings for Product Size Recommendations. eCOM@SIGIR 2019 - [i12]Benjamin Paul Chamberlain, Stephen R. Hardwick, David R. Wardrope, Fabon Dzogang, Fabio Daolio, Saúl Vargas:
Scalable Hyperbolic Recommender Systems. CoRR abs/1902.08648 (2019) - [i11]Elaine M. Bettaney, Stephen R. Hardwick, Odysseas Zisimopoulos, Benjamin Paul Chamberlain:
Fashion Outfit Generation for E-commerce. CoRR abs/1904.00741 (2019) - 2018
- [b1]Benjamin Paul Chamberlain:
Practical challenges of learning and representation for large graphs. Imperial College London, UK, 2018 - [c6]Nikolaos Aletras, Benjamin Paul Chamberlain:
Predicting Twitter User Socioeconomic Attributes with Network and Language Information. HT 2018: 20-24 - [c5]C. H. Bryan Liu, Benjamin Paul Chamberlain:
Speeding Up BigClam Implementation on SNAP. ICCSW 2018: 1:1-1:13 - [c4]Georg L. Grob, Ângelo Cardoso, C. H. Bryan Liu, Duncan A. Little
A Recurrent Neural Network Survival Model: Predicting Web User Return Time. ECML/PKDD (3) 2018: 152-168 - [i10]C. H. Bryan Liu, Benjamin Paul Chamberlain:
Online Controlled Experiments for Personalised e-Commerce Strategies: Design, Challenges, and Pitfalls. CoRR abs/1803.06258 (2018) - [i9]Nikolaos Aletras, Benjamin Paul Chamberlain:
Predicting Twitter User Socioeconomic Attributes with Network and Language Information. CoRR abs/1804.04095 (2018) - [i8]C. H. Bryan Liu, Elaine M. Bettaney, Benjamin Paul Chamberlain:
Designing Experiments to Measure Incrementality on Facebook. CoRR abs/1806.02588 (2018) - [i7]Georg L. Grob, Ângelo Cardoso, C. H. Bryan Liu, Duncan A. Little, Benjamin Paul Chamberlain:
A Recurrent Neural Network Survival Model: Predicting Web User Return Time. CoRR abs/1807.04098 (2018) - 2017
- [c3]Benjamin Paul Chamberlain, Ângelo Cardoso, C. H. Bryan Liu, Roberto Pagliari, Marc Peter Deisenroth:
Customer Lifetime Value Prediction Using Embeddings. KDD 2017: 1753-1762 - [c2]C. H. Bryan Liu, Benjamin Paul Chamberlain, Duncan A. Little
Generalising Random Forest Parameter Optimisation to Include Stability and Cost. ECML/PKDD (3) 2017: 102-113 - [c1]Benjamin Paul Chamberlain, Clive Humby, Marc Peter Deisenroth:
Probabilistic Inference of Twitter Users' Age Based on What They Follow. ECML/PKDD (3) 2017: 191-203 - [i6]Benjamin Paul Chamberlain, Ângelo Cardoso, C. H. Bryan Liu, Roberto Pagliari, Marc Peter Deisenroth:
Customer Life Time Value Prediction Using Embeddings. CoRR abs/1703.02596 (2017) - [i5]Benjamin Paul Chamberlain, James R. Clough, Marc Peter Deisenroth:
Neural Embeddings of Graphs in Hyperbolic Space. CoRR abs/1705.10359 (2017) - [i4]C. H. Bryan Liu, Benjamin Paul Chamberlain, Duncan A. Little, Ângelo Cardoso:
Generalising Random Forest Parameter Optimisation to Include Stability and Cost. CoRR abs/1706.09865 (2017) - [i3]C. H. Bryan Liu, Benjamin Paul Chamberlain:
Speeding Up BigClam Implementation on SNAP. CoRR abs/1712.01209 (2017) - 2016
- [i2]Benjamin Paul Chamberlain, Josh Levy-Kramer, Clive Humby, Marc Peter Deisenroth:
Real-Time Association Mining in Large Social Networks. CoRR abs/1601.03958 (2016) - [i1]Benjamin Paul Chamberlain, Clive Humby, Marc Peter Deisenroth:
Detecting the Age of Twitter Users. CoRR abs/1601.04621 (2016)
Coauthor Index
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