Следене
Javier Antoran
Заглавие
Позовавания
Позовавания
Година
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
2302020
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
1092020
Depth uncertainty in neural networks
J Antorán, JU Allingham, JM Hernández-Lobato
Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020
1042020
Bayesian Deep Learning via Subnetwork Inference
E Daxberger, E Nalisnick, JU Allingham, J Antorán, ...
International Conference on Machine Learning, 2021, 2020
832020
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
682022
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
262022
Disentangling and learning robust representations with natural clustering
J Antoran, A Miguel
2019 18th IEEE International Conference On Machine Learning And Applications …, 2019
162019
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
152022
Expressive yet tractable Bayesian deep learning via subnetwork inference
E Daxberger, E Nalisnick, J Allingham, J Antorán, JM Hernández-Lobato
152020
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
12*2022
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, 2024
112024
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
92022
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
82022
Variational depth search in ResNets
J Antorán, JU Allingham, JM Hernández-Lobato
arXiv preprint arXiv:2002.02797, 2020
62020
Understanding Uncertainty in Bayesian Neural Networks
JA Cabiscol
62019
SE (3) equivariant augmented coupling flows
L Midgley, V Stimper, J Antorán, E Mathieu, B Schölkopf, ...
Advances in Neural Information Processing Systems 36, 2024
52024
A probabilistic deep image prior over image space
R Barbano, J Antorán, JM Hernández-Lobato, B Jin
Fourth Symposium on Advances in Approximate Bayesian Inference, 2022
42022
Online laplace model selection revisited
JA Lin, J Antorán, JM Hernández-Lobato
arXiv preprint arXiv:2307.06093, 2023
32023
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
32022
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
22023
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