| Iq-learn: Inverse soft-q learning for imitation D Garg, S Chakraborty, C Cundy, J Song, S Ermon Advances in Neural Information Processing Systems 34, 4028-4039, 2021 | 281 | 2021 |
| Neural networks and the chomsky hierarchy G Delétang, A Ruoss, J Grau-Moya, T Genewein, LK Wenliang, E Catt, ... arXiv preprint arXiv:2207.02098, 2022 | 242 | 2022 |
| On the opportunities and challenges of foundation models for geospatial artificial intelligence G Mai, W Huang, J Sun, S Song, D Mishra, N Liu, S Gao, T Liu, G Cong, ... arXiv preprint arXiv:2304.06798, 2023 | 206 | 2023 |
| Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages Y Hu, G Mai, C Cundy, K Choi, N Lao, W Liu, G Lakhanpal, RZ Zhou, ... International Journal of Geographical Information Science 37 (11), 2289-2318, 2023 | 186 | 2023 |
| Parallelizing Linear Recurrent Neural Nets Over Sequence Length E Martin, C Cundy International Conference on Learning Representations (ICLR) 2018, 2018 | 177 | 2018 |
| On the opportunities and challenges of foundation models for geoai (vision paper) G Mai, W Huang, J Sun, S Song, D Mishra, N Liu, S Gao, T Liu, G Cong, ... ACM Transactions on Spatial Algorithms and Systems 10 (2), 1-46, 2024 | 112 | 2024 |
| Towards a foundation model for geospatial artificial intelligence (vision paper) G Mai, C Cundy, K Choi, Y Hu, N Lao, S Ermon Proceedings of the 30th International Conference on Advances in Geographic …, 2022 | 106 | 2022 |
| BCD nets: Scalable variational approaches for bayesian causal discovery C Cundy, A Grover, S Ermon Advances in Neural Information Processing Systems 34, 7095-7110, 2021 | 89 | 2021 |
| LMPriors: Pre-Trained Language Models as Task-Specific Priors K Choi*, C Cundy*, S Srivastava, S Ermon First Workshop on Foundation Models for Decision Making, Neurips 2022, 2022 | 69 | 2022 |
| Kggen: Extracting knowledge graphs from plain text with language models B Mo, K Yu, J Kazdan, J Cabezas, P Mpala, L Yu, C Cundy, C Kanatsoulis, ... arXiv preprint arXiv:2502.09956, 2025 | 31 | 2025 |
| Simulation of plants in buildings; incorporating plant-Air interactions in building energy simulation R Ward, R Choudhary, C Cundy, G Johnson, A McRobie Proc. 14th Intern Conf IBPSA-Building Simulation, 2256-63, 2015 | 29 | 2015 |
| Sequencematch: Imitation learning for autoregressive sequence modelling with backtracking C Cundy, S Ermon The Twelfth International Conference on Learning Representations, 2024 | 23 | 2024 |
| Predicting human deliberative judgments with machine learning O Evans, A Stuhlmüller, C Cundy, R Carey, Z Kenton, T McGrath, ... Future of Humanity Institute, 2018 | 15 | 2018 |
| Neural networks and the chomsky hierarchy, 2023 G Delétang, A Ruoss, J Grau-Moya, T Genewein, LK Wenliang, E Catt, ... URL https://arxiv. org/abs/2207.02098, 0 | 12 | |
| A physics-informed machine learning model for the prediction of drop breakup in two-phase flows C Cundy, S Mirjalili, C Laurent, S Ermon, G Iaccarino, A Mani International Journal of Multiphase Flow 180, 104934, 2024 | 10 | 2024 |
| Planning in a recurrent neural network that plays Sokoban M Taufeeque, P Quirke, M Li, C Cundy, AD Tucker, A Gleave, ... arXiv preprint arXiv:2407.15421, 2024 | 8 | 2024 |
| Privacy-constrained policies via mutual information regularized policy gradients CJ Cundy, R Desai, S Ermon International Conference on Artificial Intelligence and Statistics, 2809-2817, 2024 | 7 | 2024 |
| Flexible approximate inference via stratified normalizing flows C Cundy, S Ermon Conference on Uncertainty in Artificial Intelligence, 1288-1297, 2020 | 7 | 2020 |
| Beyond Bayes-optimality: meta-learning what you know you don't know J Grau-Moya, G Delétang, M Kunesch, T Genewein, E Catt, K Li, A Ruoss, ... arXiv preprint arXiv:2209.15618, 2022 | 4 | 2022 |
| Exploring Hierarchy-Aware Inverse Reinforcement Learning C Cundy, D Filan First Workshop on Goal Specifications for Reinforcement Learning, ICML 2018 …, 2018 | 4 | 2018 |
