| Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty U Bhatt, J Antorán, Y Zhang, QV Liao, P Sattigeri, R Fogliato, ... 2021 AAAI/ACM Conference on AI, Ethics, and Society, 2020 | 299 | 2020 |
| Getting a CLUE: A Method for Explaining Uncertainty Estimates J Antorán, U Bhatt, T Adel, A Weller, JM Hernández-Lobato International Conference on Learning Representations (ICLR), 2021, 2020 | 143 | 2020 |
| Depth uncertainty in neural networks J Antorán, JU Allingham, JM Hernández-Lobato Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020 | 127 | 2020 |
| Bayesian Deep Learning via Subnetwork Inference E Daxberger, E Nalisnick, JU Allingham, J Antorán, ... International Conference on Machine Learning, 2021, 2020 | 117 | 2020 |
| Deep end-to-end causal inference T Geffner, J Antoran, A Foster, W Gong, C Ma, E Kiciman, A Sharma, ... arXiv preprint arXiv:2202.02195, 2022 | 87 | 2022 |
| Adapting the linearised laplace model evidence for modern deep learning J Antorán, D Janz, JU Allingham, E Daxberger, RR Barbano, E Nalisnick, ... International Conference on Machine Learning, 796-821, 2022 | 36 | 2022 |
| Sampling-based inference for large linear models, with application to linearised Laplace J Antorán, S Padhy, R Barbano, E Nalisnick, D Janz, ... arXiv preprint arXiv:2210.04994, 2022 | 23 | 2022 |
| Se (3) equivariant augmented coupling flows L Midgley, V Stimper, J Antorán, E Mathieu, B Schölkopf, ... Advances in Neural Information Processing Systems 36, 79200-79225, 2023 | 21 | 2023 |
| Sampling from Gaussian process posteriors using stochastic gradient descent JA Lin, J Antorán, S Padhy, D Janz, JM Hernández-Lobato, A Terenin Advances in Neural Information Processing Systems 36, 36886-36912, 2023 | 20 | 2023 |
| Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior J Antorán, R Barbano, J Leuschner, JM Hernández-Lobato, B Jin arXiv preprint arXiv:2203.00479, 2022 | 17* | 2022 |
| Expressive yet tractable Bayesian deep learning via subnetwork inference E Daxberger, E Nalisnick, J Allingham, J Antorán, JM Hernández-Lobato | 15 | 2020 |
| Disentangling and learning robust representations with natural clustering J Antoran, A Miguel 2019 18th IEEE International Conference On Machine Learning And Applications …, 2019 | 15 | 2019 |
| Understanding Uncertainty in Bayesian Neural Networks JA Cabiscol | 15 | 2019 |
| 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 |
| Bayesian experimental design for computed tomography with the linearised deep image prior R Barbano, J Leuschner, J Antorán, B Jin, JM Hernández-Lobato Adaptive Experimental Design and Active Learning workshop at ICML 2022, 2022 | 8 | 2022 |
| Stochastic gradient descent for Gaussian processes done right JA Lin, S Padhy, J Antorán, A Tripp, A Terenin, C Szepesvári, ... arXiv preprint arXiv:2310.20581, 2023 | 7 | 2023 |
| Variational depth search in ResNets J Antorán, JU Allingham, JM Hernández-Lobato arXiv preprint arXiv:2002.02797, 2020 | 6 | 2020 |
| & Xiang, A.(2021, July). Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty U Bhatt, J Antorán, Y Zhang, QV Liao, P Sattigeri, R Fogliato Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 401-413, 0 | 6 | |
| Online laplace model selection revisited JA Lin, J Antorán, JM Hernández-Lobato arXiv preprint arXiv:2307.06093, 2023 | 5 | 2023 |
| Fast and painless image reconstruction in deep image prior subspaces R Barbano, J Antorán, J Leuschner, JM Hernández-Lobato, Ž Kereta, ... arXiv preprint arXiv:2302.10279, 2023 | 4 | 2023 |
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeE-mail confirmado em cam.ac.uk