Benjamin Paul Chamberlain

Publication Activity (10 Years)

Years Active: 2016-2023
Publications (10 Years): 43

Top Venues

CoRR

ICLR

ECML/PKDD (3)

eCOM@SIGIR

Imperial College London, UK (PHD)

Education History

Imperial College London, UK

PHD

Publications

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)
Edoardo Cetin, Benjamin Paul Chamberlain, Michael M. Bronstein, Jonathan J. Hunt
Hyperbolic Deep Reinforcement Learning. ICLR (2023)
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)
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)
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)
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)
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)
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)
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)
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)
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein
Understanding over-squashing and bottlenecks on graphs via curvature. CoRR (2021)
Benjamin Paul Chamberlain, James Rowbottom, Maria I. Gorinova, Stefan Webb, Emanuele Rossi, Michael M. Bronstein
GRAND: Graph Neural Diffusion. 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)
C. H. Bryan Liu, Benjamin Paul Chamberlain, Emma J. McCoy
What is the Value of Experimentation and Measurement? Data Sci. Eng. 5 (2) (2020)
Benjamin Paul Chamberlain, Emanuele Rossi, Dan Shiebler, Suvash Sedhain, Michael M. Bronstein
Tuning Word2vec for Large Scale Recommendation Systems. CoRR (2020)
Elaine M. Bettaney, Stephen R. Hardwick, Odysseas Zisimopoulos, Benjamin Paul Chamberlain
Fashion Outfit Generation for E-Commerce. ECML/PKDD (5) (2020)
Benjamin Paul Chamberlain, Emanuele Rossi, Dan Shiebler, Suvash Sedhain, Michael M. Bronstein
Tuning Word2vec for Large Scale Recommendation Systems. RecSys (2020)
Benjamin Paul Chamberlain, Stephen R. Hardwick, David R. Wardrope, Fabon Dzogang, Fabio Daolio, Saúl Vargas
Scalable Hyperbolic Recommender Systems. CoRR (2019)
Elaine M. Bettaney, Stephen R. Hardwick, Odysseas Zisimopoulos, Benjamin Paul Chamberlain
Fashion Outfit Generation for E-commerce. CoRR (2019)
Kallirroi Dogani, Matteo Tomassetti, Saúl Vargas, Benjamin Paul Chamberlain, Sofie De Cnudde
Learning Embeddings for Product Size Recommendations. eCOM@SIGIR (2019)
C. H. Bryan Liu, Benjamin Paul Chamberlain
What is the Value of Experimentation & Measurement? ICDM (2019)
Elaine M. Bettaney, Stephen R. Hardwick, Benjamin Paul Chamberlain, Odysseas Zisimopoulos
Fashion Outfit Generation for E-commerce. eCOM@SIGIR (2019)
C. H. Bryan Liu, Benjamin Paul Chamberlain
Online Controlled Experiments for Personalised e-Commerce Strategies: Design, Challenges, and Pitfalls. CoRR (2018)
Nikolaos Aletras, Benjamin Paul Chamberlain
Predicting Twitter User Socioeconomic Attributes with Network and Language Information. CoRR (2018)
C. H. Bryan Liu, Benjamin Paul Chamberlain
Speeding Up BigClam Implementation on SNAP. ICCSW (2018)
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. ECML/PKDD (3) (2018)
C. H. Bryan Liu, Elaine M. Bettaney, Benjamin Paul Chamberlain
Designing Experiments to Measure Incrementality on Facebook. CoRR (2018)
Nikolaos Aletras, Benjamin Paul Chamberlain
Predicting Twitter User Socioeconomic Attributes with Network and Language Information. HT (2018)
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 (2018)
C. H. Bryan Liu, Benjamin Paul Chamberlain, Duncan A. Little, Ângelo Cardoso
Generalising Random Forest Parameter Optimisation to Include Stability and Cost. ECML/PKDD (3) (2017)
C. H. Bryan Liu, Benjamin Paul Chamberlain, Duncan A. Little, Ângelo Cardoso
Generalising Random Forest Parameter Optimisation to Include Stability and Cost. CoRR (2017)
C. H. Bryan Liu, Benjamin Paul Chamberlain
Speeding Up BigClam Implementation on SNAP. CoRR (2017)
Benjamin Paul Chamberlain, Clive Humby, Marc Peter Deisenroth
Probabilistic Inference of Twitter Users' Age Based on What They Follow. ECML/PKDD (3) (2017)
Benjamin Paul Chamberlain, James R. Clough, Marc Peter Deisenroth
Neural Embeddings of Graphs in Hyperbolic Space. CoRR (2017)
Benjamin Paul Chamberlain, Ângelo Cardoso, C. H. Bryan Liu, Roberto Pagliari, Marc Peter Deisenroth
Customer Life Time Value Prediction Using Embeddings. CoRR (2017)
Benjamin Paul Chamberlain, Ângelo Cardoso, C. H. Bryan Liu, Roberto Pagliari, Marc Peter Deisenroth
Customer Lifetime Value Prediction Using Embeddings. KDD (2017)
Benjamin Paul Chamberlain, Josh Levy-Kramer, Clive Humby, Marc Peter Deisenroth
Real-Time Association Mining in Large Social Networks. CoRR (2016)
Benjamin Paul Chamberlain, Clive Humby, Marc Peter Deisenroth
Detecting the Age of Twitter Users. CoRR (2016)

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Publication Activity (10 Years)

Top Topics

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Benjamin Paul Chamberlain