| Random Feature Expansions for Deep Gaussian Processes K Cutajar, EV Bonilla, P Michiardi, M Filippone Proceedings of the 34th International Conference on Machine Learning (ICML), 2017 | 188 | 2017 |
| Deep Gaussian Processes for Multi-fidelity Modeling K Cutajar, M Pullin, A Damianou, N Lawrence, J González Third Bayesian Deep Learning Workshop, Advances in Neural Information …, 2019 | 145 | 2019 |
| Preconditioning kernel matrices K Cutajar, M Osborne, J Cunningham, M Filippone Proceedings of the 33rd International Conference on Machine Learning (ICML …, 2016 | 94 | 2016 |
| AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models K Krauth, EV Bonilla, K Cutajar, M Filippone Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence …, 2017 | 67 | 2017 |
| Entropic Trace Estimates for Log Determinants J Fitzsimons, D Granziol, K Cutajar, M Osborne, M Filippone, S Roberts Machine Learning and Knowledge Discovery in Databases - European Conference …, 2017 | 26 | 2017 |
| Bayesian Inference of Log Determinants J Fitzsimons, K Cutajar, M Osborne, S Roberts, M Filippone Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence …, 2017 | 17 | 2017 |
| Inherently Interpretable Time Series Classification via Multiple Instance Learning J Early, GKC Cheung, K Cutajar, H Xie, J Kandola, N Twomey The Twelfth International Conference on Learning Representations (ICLR 2024), 2023 | 6 | 2023 |
| Broadening the Scope of Gaussian Processes for Large-Scale Learning K Cutajar Sorbonne University, 2019 | 5 | 2019 |
| Low-count Time Series Anomaly Detection P Renz, K Cutajar, N Twomey, G Cheung, H Xie | | 2023 |
| Accelerating Deep Gaussian Process Inference with Arc-Cosine Kernels K Cutajar, EV Bonilla, P Michiardi, M Filippone First Bayesian Deep Learning Workshop, Advances in Neural Information …, 2016 | | 2016 |
