Javier Antorán

According to our database, Javier Antorán authored at least 22 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A Generative Model of Symmetry Transformations.

James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato

CoRR, 2024

2023

Stochastic Gradient Descent for Gaussian Processes Done Right.

Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz

CoRR, 2023

Online Laplace Model Selection Revisited.

Jihao Andreas Lin, Javier Antorán, José Miguel Hernández-Lobato

CoRR, 2023

Fast and Painless Image Reconstruction in Deep Image Prior Subspaces.

Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Zeljko Kereta, Bangti Jin

CoRR, 2023

SE(3) Equivariant Augmented Coupling Flows.

Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent.

Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Sampling-based inference for large linear models, with application to linearised Laplace.

Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior.

Riccardo Barbano, Johannes Leuschner, Javier Antorán, Bangti Jin, José Miguel Hernández-Lobato

CoRR, 2022

A Probabilistic Deep Image Prior for Computational Tomography.

Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin

CoRR, 2022

Deep End-to-end Causal Inference.

Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang

CoRR, 2022

Adapting the Linearised Laplace Model Evidence for Modern Deep Learning.

Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato

Proceedings of the International Conference on Machine Learning, 2022

2021

Depth Uncertainty Networks for Active Learning.

Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato

CoRR, 2021

Bayesian Deep Learning via Subnetwork Inference.

Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato

Proceedings of the 38th International Conference on Machine Learning, 2021

Getting a CLUE: A Method for Explaining Uncertainty Estimates.

Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato

Proceedings of the 9th International Conference on Learning Representations, 2021

Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not.

Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato

Proceedings of the I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 2021

Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty.

Umang Bhatt, Javier Antorán, Yunfeng Zhang, Q. Vera Liao, Prasanna Sattigeri, Riccardo Fogliato, Gabrielle Gauthier Melançon, Ranganath Krishnan, Jason Stanley, Omesh Tickoo, Lama Nachman, Rumi Chunara, Madhulika Srikumar, Adrian Weller, Alice Xiang

Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

2020

Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty.

Umang Bhatt, Yunfeng Zhang, Javier Antorán, Q. Vera Liao, Prasanna Sattigeri, Riccardo Fogliato, Gabrielle Gauthier Melançon, Ranganath Krishnan, Jason Stanley, Omesh Tickoo, Lama Nachman, Rumi Chunara, Adrian Weller, Alice Xiang

CoRR, 2020

Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference.

Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato

CoRR, 2020

Variational Depth Search in ResNets.

Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato

CoRR, 2020

Depth Uncertainty in Neural Networks.

Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Disentangling in Variational Autoencoders with Natural Clustering.

Javier Antorán, Antonio Miguel

CoRR, 2019

Disentangling and Learning Robust Representations with Natural Clustering.

Javier Antorán, Antonio Miguel

Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019