Detecting the Age of Twitter Users. - Benjamin Paul Chamberlain; Clive Humby; Marc Peter Deisenroth (CoRR, 2016)

Real-Time Association Mining in Large Social Networks. - Benjamin Paul Chamberlain; Josh Levy-Kramer; Clive Humby; Marc Peter Deisenroth (CoRR, 2016)

Customer Lifetime Value Prediction Using Embeddings. - Benjamin Paul Chamberlain; Ângelo Cardoso; C. H. Bryan Liu; Roberto Pagliari; Marc Peter Deisenroth (KDD, 2017)

Probabilistic Inference of Twitter Users' Age Based on What They Follow. - Benjamin Paul Chamberlain; Clive Humby; Marc Peter Deisenroth (ECML/PKDD (3), 2017)

Generalising Random Forest Parameter Optimisation to Include Stability and Cost. - C. H. Bryan Liu; Benjamin Paul Chamberlain; Duncan A. Little; Ângelo Cardoso (ECML/PKDD (3), 2017)

Neural Embeddings of Graphs in Hyperbolic Space. - Benjamin Paul Chamberlain; James R. Clough; Marc Peter Deisenroth (CoRR, 2017)

Customer Life Time Value Prediction Using Embeddings. - Benjamin Paul Chamberlain; Ângelo Cardoso; C. H. Bryan Liu; Roberto Pagliari; Marc Peter Deisenroth (CoRR, 2017)

Predicting Twitter User Socioeconomic Attributes with Network and Language Information. - Nikolaos Aletras; Benjamin Paul Chamberlain (HT, 2018)

Speeding Up BigClam Implementation on SNAP. - C. H. Bryan Liu; Benjamin Paul Chamberlain (ICCSW, 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 (ECML/PKDD (3), 2018)

Online Controlled Experiments for Personalised e-Commerce Strategies: Design, Challenges, and Pitfalls. - C. H. Bryan Liu; Benjamin Paul Chamberlain (CoRR, 2018)

Designing Experiments to Measure Incrementality on Facebook. - C. H. Bryan Liu; Elaine M. Bettaney; Benjamin Paul Chamberlain (CoRR, 2018)

Practical challenges of learning and representation for large graphs. - Benjamin Paul Chamberlain (2018)

What is the Value of Experimentation & Measurement? - C. H. Bryan Liu; Benjamin Paul Chamberlain (ICDM, 2019)

Fashion Outfit Generation for E-commerce. - Elaine M. Bettaney; Stephen R. Hardwick; Benjamin Paul Chamberlain; Odysseas Zisimopoulos (eCOM@SIGIR, 2019)

Learning Embeddings for Product Size Recommendations. - Kallirroi Dogani; Matteo Tomassetti; Saúl Vargas; Benjamin Paul Chamberlain; Sofie De Cnudde (eCOM@SIGIR, 2019)

Scalable Hyperbolic Recommender Systems. - Benjamin Paul Chamberlain; Stephen R. Hardwick; David R. Wardrope; Fabon Dzogang; Fabio Daolio; Saúl Vargas (CoRR, 2019)

Tuning Word2vec for Large Scale Recommendation Systems. - Benjamin Paul Chamberlain; Emanuele Rossi; Dan Shiebler; Suvash Sedhain; Michael M. Bronstein (RecSys, 2020)

SIGN: Scalable Inception Graph Neural Networks. - Emanuele Rossi; Fabrizio Frasca; Ben Chamberlain 001; Davide Eynard; Michael M. Bronstein; Federico Monti (CoRR, 2020)

Temporal Graph Networks for Deep Learning on Dynamic Graphs. - Emanuele Rossi; Ben Chamberlain 001; Fabrizio Frasca; Davide Eynard; Federico Monti; Michael M. Bronstein (CoRR, 2020)

What is the Value of Experimentation and Measurement? - C. H. Bryan Liu; Benjamin Paul Chamberlain; Emma J. McCoy (Data Sci. Eng., 2020)

GRAND: Graph Neural Diffusion. - Ben Chamberlain 001; James Rowbottom; Maria I. Gorinova 001; Michael M. Bronstein; Stefan Webb; Emanuele Rossi (ICML, 2021)

Beltrami Flow and Neural Diffusion on Graphs. - Ben Chamberlain 001; James Rowbottom; Davide Eynard; Francesco Di Giovanni; Xiaowen Dong 001; Michael M. Bronstein (NeurIPS, 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 001; Yuanpu Xie; Jonathan Hunt; Michael M. Bronstein; Wenzhe Shi (RecSys, 2021)

The 2021 RecSys Challenge Dataset: Fairness is not optional. - Luca Belli; Alykhan Tejani; Frank Portman; Alexandre Lung-Yut-Fong; Ben Chamberlain 001; Yuanpu Xie; Kristian Lum; Jonathan Hunt; Michael M. Bronstein; Vito Walter Anelli; Saikishore Kalloori; Bruce Ferwerda; et al. (RecSys Challenge, 2021)

Understanding over-squashing and bottlenecks on graphs via curvature. - Jake Topping; Francesco Di Giovanni; Benjamin Paul Chamberlain; Xiaowen Dong 001; Michael M. Bronstein (ICLR, 2022)

Graph-Coupled Oscillator Networks. - T. Konstantin Rusch; Ben Chamberlain 001; James Rowbottom; Siddhartha Mishra; Michael M. Bronstein (ICML, 2022)

On the Unreasonable Effectiveness of Feature Propagation in Learning on Graphs With Missing Node Features. - Emanuele Rossi; Henry Kenlay; Maria I. Gorinova 001; Benjamin Paul Chamberlain; Xiaowen Dong 001; Michael M. Bronstein (LoG, 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 (NeurIPS, 2022)

Graph Neural Networks as Gradient Flows. - Francesco Di Giovanni; James Rowbottom; Benjamin Paul Chamberlain; Thomas Markovich; Michael M. Bronstein (CoRR, 2022)

Hyperbolic Deep Reinforcement Learning. - Edoardo Cetin; Benjamin Paul Chamberlain; Michael M. Bronstein; Jonathan J. Hunt (ICLR, 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 (ICLR, 2023)

Gradient Gating for Deep Multi-Rate Learning on Graphs. - T. Konstantin Rusch; Benjamin Paul Chamberlain; Michael W. Mahoney; Michael M. Bronstein; Siddhartha Mishra (ICLR, 2023)

The Second Learning on Graphs Conference: Preface. - Soledad Villar; Benjamin Paul Chamberlain; Yuanqi Du; Hannes Stärk; Chaitanya K. Joshi; Andreea Deac; Iulia Duta; Joshua Robinson 001; Yanqiao Zhu 001; Kexin Huang; Michelle M. Li; Sofia Bourhim; et al. (LoG, 2023)

Understanding convolution on graphs via energies. - Francesco Di Giovanni; James Rowbottom; Benjamin Paul Chamberlain; Thomas Markovich; Michael M. Bronstein (Trans. Mach. Learn. Res., 2023)