| Neural embeddings of graphs in hyperbolic space BP Chamberlain, J Clough, MP Deisenroth arXiv preprint arXiv:1705.10359, 2017 | 130 | 2017 |
| Temporal graph networks for deep learning on dynamic graphs E Rossi, B Chamberlain, F Frasca, D Eynard, F Monti, M Bronstein arXiv preprint arXiv:2006.10637, 2020 | 108 | 2020 |
| Sign: Scalable inception graph neural networks E Rossi, F Frasca, B Chamberlain, D Eynard, M Bronstein, F Monti arXiv preprint arXiv:2004.11198, 2020 | 78 | 2020 |
| Customer lifetime value prediction using embeddings BP Chamberlain, A Cardoso, CHB Liu, R Pagliari, MP Deisenroth Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017 | 72 | 2017 |
| Predicting twitter user socioeconomic attributes with network and language information N Aletras, BP Chamberlain Proceedings of the 29th on Hypertext and Social Media, 20-24, 2018 | 51 | 2018 |
| Sign: Scalable inception graph neural networks F Frasca, E Rossi, D Eynard, B Chamberlain, M Bronstein, F Monti arXiv preprint arXiv:2004.11198, 2020 | 50 | 2020 |
| Scalable hyperbolic recommender systems BP Chamberlain, SR Hardwick, DR Wardrope, F Dzogang, F Daolio, ... arXiv preprint arXiv:1902.08648, 2019 | 29 | 2019 |
| GRAND: Graph Neural Diffusion BP Chamberlain, J Rowbottom, M Gorinova, S Webb, E Rossi, ... The 38th International Conference on Machine Learning (ICML 2021), 2021 | 26 | 2021 |
| Probabilistic inference of twitter users’ age based on what they follow BP Chamberlain, C Humby, MP Deisenroth Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017 | 23* | 2017 |
| Generalising random forest parameter optimisation to include stability and cost CH Liu, BP Chamberlain, DA Little, Â Cardoso Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017 | 22 | 2017 |
| Real-time community detection in full social networks on a laptop BP Chamberlain, J Levy-Kramer, C Humby, MP Deisenroth PloS one 13 (1), e0188702, 2018 | 17 | 2018 |
| Fashion Outfit Generation for E-commerce EM Bettaney, SR Hardwick, O Zisimopoulos, BP Chamberlain Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019 | 16 | 2019 |
| Understanding over-squashing and bottlenecks on graphs via curvature J Topping, F Di Giovanni, BP Chamberlain, X Dong, MM Bronstein The Tenth International Conference on Learning Representations (ICLR 2022), 2021 | 14 | 2021 |
| Learning Embeddings for Product Size Recommendations K Dogani, M Tomassetti, S De Cnudde, S Vargas, B Chamberlain eCOM@SIGIR, 2019 | 12 | 2019 |
| A recurrent neural network survival model: Predicting web user return time GL Grob, Â Cardoso, CH Liu, DA Little, BP Chamberlain Joint European Conference on Machine Learning and Knowledge Discovery in …, 2018 | 9 | 2018 |
| RecSys 2021 Challenge Workshop: Fairness-aware engagement prediction at scale on Twitter’s Home Timeline VW Anelli, S Kalloori, B Ferwerda, L Belli, A Tejani, F Portman, ... Fifteenth ACM Conference on Recommender Systems, 819-824, 2021 | 8 | 2021 |
| The 2021 RecSys Challenge Dataset: Fairness is not optional L Belli, A Tejani*, F Portman*, A Lung-Yut-Fong*, B Chamberlain, Y Xie, ... RecSysChallenge'21: Proceedings of the Recommender Systems Challenge 2021, 1-6, 2021 | 7 | 2021 |
| Tuning Word2vec for Large Scale Recommendation Systems BP Chamberlain, E Rossi, D Shiebler, S Sedhain, MM Bronstein In Fourteenth ACM Conference on Recommender Systems (pp. 732-737), 2020 | 6 | 2020 |
| Online controlled experiments for personalised e-commerce strategies: Design, challenges, and pitfalls CH Liu, BP Chamberlain arXiv preprint arXiv:1803.06258, 2018 | 5 | 2018 |
| On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features E Rossi, H Kenlay, MI Gorinova, BP Chamberlain, X Dong, M Bronstein arXiv preprint arXiv:2111.12128, 2021 | 4 | 2021 |
Michael BronsteinDeepMind Professor of AI, University of Oxford / Head Graph ML Research, TwitterVerified email at cs.ox.ac.uk
