| Depth uncertainty in neural networks J Antorán*, J Allingham*, JM Hernández-Lobato Advances in neural information processing systems 33, 10620-10634, 2020 | 117 | 2020 |
| Bayesian deep learning via subnetwork inference E Daxberger, E Nalisnick*, JU Allingham*, J Antorán*, ... International Conference on Machine Learning, 2510-2521, 2021 | 106* | 2021 |
| Adapting the linearised laplace model evidence for modern deep learning J Antorán, D Janz*, JU Allingham*, E Daxberger, RR Barbano, ... International Conference on Machine Learning, 796-821, 2022 | 26 | 2022 |
| A simple zero-shot prompt weighting technique to improve prompt ensembling in text-image models JU Allingham*, J Ren*, MW Dusenberry, X Gu, Y Cui, D Tran, JZ Liu, ... International Conference on Machine Learning, 547-568, 2023 | 23 | 2023 |
| Sparse MoEs meet efficient ensembles JU Allingham, F Wenzel, ZE Mariet, B Mustafa, J Puigcerver, N Houlsby, ... Transactions on Machine Learning Research, 2021 | 21 | 2021 |
| Deep classifiers with label noise modeling and distance awareness V Fortuin, M Collier, F Wenzel, J Allingham, J Liu, D Tran, ... arXiv preprint arXiv:2110.02609, 2021 | 11 | 2021 |
| Linearised laplace inference in networks with normalisation layers and the neural g-prior J Antorán, JU Allingham, D Janz, E Daxberger, E Nalisnick, ... Fourth Symposium on Advances in Approximate Bayesian Inference, 2022 | 9 | 2022 |
| Variational depth search in ResNets J Antorán, JU Allingham, JM Hernández-Lobato arXiv preprint arXiv:2002.02797, 2020 | 6 | 2020 |
| Towards anytime classification in early-exit architectures by enforcing conditional monotonicity M Jazbec, J Allingham, D Zhang, E Nalisnick Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
| Unsupervised automatic dataset repair JU Allingham Master’s thesis in advanced computer science, Computer Laboratory …, 2018 | 4 | 2018 |
| Addressing bias in active learning with depth uncertainty networks... or not C Murray, JU Allingham, J Antorán, JM Hernández-Lobato I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 59-63, 2022 | 3 | 2022 |
| Learning generative models with invariance to symmetries JU Allingham, J Antoran, S Padhy, E Nalisnick, JM Hernández-Lobato NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations, 2022 | 2 | 2022 |
| Model AI Assignments 2020 TW Neller, S Keeley, M Guerzhoy, W Hoenig, J Li, S Koenig, A Soni, ... Proceedings of the AAAI conference on artificial intelligence 34 (09), 13509 …, 2020 | 2 | 2020 |
| A Product of Experts Approach to Early-Exit Ensembles JU Allingham, E Nalisnick Technical report, 2022 | 1 | 2022 |
| Depth Uncertainty Networks for Active Learning C Murray, JU Allingham, J Antorán, JM Hernández-Lobato arXiv preprint arXiv:2112.06796, 2021 | 1 | 2021 |
| Zero-Shot Prompt Ensembling for Zero-Shot Classification with Text-Image Models J Ren, Z Liu, JU Allingham, MW Dusenberry, D Tran, Y Cui, ... US Patent App. 18/421,672, 2024 | | 2024 |
| A Generative Model of Symmetry Transformations JU Allingham, BK Mlodozeniec, S Padhy, J Antorán, D Krueger, ... arXiv preprint arXiv:2403.01946, 2024 | | 2024 |
| Ensembling mixture-of-experts neural networks R Jenatton, CR Ruiz, D Tran, JU Allingham, F Wenzel, ZE Mariet, ... US Patent App. 17/960,780, 2023 | | 2023 |
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
