Benjamin Paul Chamberlain

According to our database, Benjamin Paul Chamberlain authored at least 33 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Gradient Gating for Deep Multi-Rate Learning on Graphs.

T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra

Proceedings of the Eleventh International Conference on Learning Representations, 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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Hyperbolic Deep Reinforcement Learning.

Edoardo Cetin, Benjamin Paul Chamberlain, Michael M. Bronstein, Jonathan J. Hunt

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Graph Neural Networks as Gradient Flows.

Francesco Di Giovanni, James Rowbottom, Benjamin Paul Chamberlain, Thomas Markovich, Michael M. Bronstein

CoRR, 2022

Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs.

Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Lió, Michael M. Bronstein

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 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

Proceedings of the Learning on Graphs Conference, 2022

Graph-Coupled Oscillator Networks.

T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein

Proceedings of the International Conference on Machine Learning, 2022

Understanding over-squashing and bottlenecks on graphs via curvature.

Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

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

Proceedings of the RecSys Challenge 2021: Proceedings of the Recommender Systems Challenge 2021, 2021

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

Proceedings of the RecSys '21: Fifteenth ACM Conference on Recommender Systems, Amsterdam, The Netherlands, 27 September 2021, 2021

Beltrami Flow and Neural Diffusion on Graphs.

Ben Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, Michael M. Bronstein

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

GRAND: Graph Neural Diffusion.

Ben Chamberlain, James Rowbottom, Maria I. Gorinova, Michael M. Bronstein, Stefan Webb, Emanuele Rossi

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

What is the Value of Experimentation and Measurement?

C. H. Bryan Liu, Benjamin Paul Chamberlain, Emma J. McCoy

Data Sci. Eng., 2020

Temporal Graph Networks for Deep Learning on Dynamic Graphs.

Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, Michael M. Bronstein

CoRR, 2020

SIGN: Scalable Inception Graph Neural Networks.

Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, Davide Eynard, Michael M. Bronstein, Federico Monti

CoRR, 2020

Tuning Word2vec for Large Scale Recommendation Systems.

Benjamin Paul Chamberlain, Emanuele Rossi, Dan Shiebler, Suvash Sedhain, Michael M. Bronstein

Proceedings of the RecSys 2020: Fourteenth ACM Conference on Recommender Systems, 2020

Fashion Outfit Generation for E-Commerce.

Elaine M. Bettaney, Stephen R. Hardwick, Odysseas Zisimopoulos, Benjamin Paul Chamberlain

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track, 2020

2019

Scalable Hyperbolic Recommender Systems.

Benjamin Paul Chamberlain, Stephen R. Hardwick, David R. Wardrope, Fabon Dzogang, Fabio Daolio, Saúl Vargas

CoRR, 2019

Learning Embeddings for Product Size Recommendations.

Kallirroi Dogani, Matteo Tomassetti, Saúl Vargas, Benjamin Paul Chamberlain, Sofie De Cnudde

Proceedings of the SIGIR 2019 Workshop on eCommerce, 2019

What is the Value of Experimentation & Measurement?

C. H. Bryan Liu, Benjamin Paul Chamberlain

Proceedings of the 2019 IEEE International Conference on Data Mining, 2019

2018

Practical challenges of learning and representation for large graphs.

Benjamin Paul Chamberlain

PhD thesis, 2018

Designing Experiments to Measure Incrementality on Facebook.

C. H. Bryan Liu, Elaine M. Bettaney, Benjamin Paul Chamberlain

CoRR, 2018

Online Controlled Experiments for Personalised e-Commerce Strategies: Design, Challenges, and Pitfalls.

C. H. Bryan Liu, Benjamin Paul Chamberlain

CoRR, 2018

A Recurrent Neural Network Survival Model: Predicting Web User Return Time.

Georg L. Grob, Ângelo Cardoso, C. H. Bryan Liu, Duncan A. Little, Benjamin Paul Chamberlain

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

Speeding Up BigClam Implementation on SNAP.

C. H. Bryan Liu, Benjamin Paul Chamberlain

Proceedings of the 2018 Imperial College Computing Student Workshop, 2018

Predicting Twitter User Socioeconomic Attributes with Network and Language Information.

Nikolaos Aletras, Benjamin Paul Chamberlain

Proceedings of the 29th on Hypertext and Social Media, 2018

2017

Customer Life Time Value Prediction Using Embeddings.

Benjamin Paul Chamberlain, Ângelo Cardoso, C. H. Bryan Liu, Roberto Pagliari, Marc Peter Deisenroth

CoRR, 2017

Neural Embeddings of Graphs in Hyperbolic Space.

Benjamin Paul Chamberlain, James R. Clough, Marc Peter Deisenroth

CoRR, 2017

Generalising Random Forest Parameter Optimisation to Include Stability and Cost.

C. H. Bryan Liu, Benjamin Paul Chamberlain, Duncan A. Little, Ângelo Cardoso

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Probabilistic Inference of Twitter Users' Age Based on What They Follow.

Benjamin Paul Chamberlain, Clive Humby, Marc Peter Deisenroth

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Customer Lifetime Value Prediction Using Embeddings.

Benjamin Paul Chamberlain, Ângelo Cardoso, C. H. Bryan Liu, Roberto Pagliari, Marc Peter Deisenroth

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

2016

Real-Time Association Mining in Large Social Networks.

Benjamin Paul Chamberlain, Josh Levy-Kramer, Clive Humby, Marc Peter Deisenroth

CoRR, 2016

Detecting the Age of Twitter Users.

Benjamin Paul Chamberlain, Clive Humby, Marc Peter Deisenroth

CoRR, 2016