Javier Antorán

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 34

Top Topics

Top Venues

Trans. Mach. Learn. Res.

Publications

Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin, Zeljko Kereta
Image Reconstruction via Deep Image Prior Subspaces. Trans. Mach. Learn. Res. 2024 (2024)
Javier Antorán
Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks. CoRR (2024)
Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
Stochastic Gradient Descent for Gaussian Processes Done Right. ICLR (2024)
Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Agrin Hilmkil, Joel Jennings, Meyer Scetbon, Miltiadis Allamanis, Cheng Zhang
Deep End-to-end Causal Inference. Trans. Mach. Learn. Res. 2024 (2024)
Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, Javier Antorán, José Miguel Hernández-Lobato
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes. CoRR (2024)
James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato
A Generative Model of Symmetry Transformations. CoRR (2024)
Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. CoRR (2023)
Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato
SE(3) Equivariant Augmented Coupling Flows. CoRR (2023)
Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior. Trans. Mach. Learn. Res. 2023 (2023)
Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
Stochastic Gradient Descent for Gaussian Processes Done Right. CoRR (2023)
Jihao Andreas Lin, Javier Antorán, José Miguel Hernández-Lobato
Online Laplace Model Selection Revisited. CoRR (2023)
Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato
SE(3) Equivariant Augmented Coupling Flows. NeurIPS (2023)
Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. NeurIPS (2023)
Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato
Sampling-based inference for large linear models, with application to linearised Laplace. ICLR (2023)
Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Zeljko Kereta, Bangti Jin
Fast and Painless Image Reconstruction in Deep Image Prior Subspaces. CoRR (2023)
Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin
A Probabilistic Deep Image Prior for Computational Tomography. CoRR (2022)
Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. CoRR (2022)
Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato
Sampling-based inference for large linear models, with application to linearised Laplace. CoRR (2022)
Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. ICML (2022)
Riccardo Barbano, Johannes Leuschner, Javier Antorán, Bangti Jin, José Miguel Hernández-Lobato
Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior. CoRR (2022)
Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not. CoRR (2021)
Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
Depth Uncertainty Networks for Active Learning. CoRR (2021)
Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
Bayesian Deep Learning via Subnetwork Inference. ICML (2021)
Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato
Getting a CLUE: A Method for Explaining Uncertainty Estimates. ICLR (2021)
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
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty. AIES (2021)
Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not. ICBINB@NeurIPS (2021)
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
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty. CoRR (2020)
Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato
Depth Uncertainty in Neural Networks. NeurIPS (2020)
Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato
Getting a CLUE: A Method for Explaining Uncertainty Estimates. CoRR (2020)
Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato
Depth Uncertainty in Neural Networks. CoRR (2020)
Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato
Variational Depth Search in ResNets. CoRR (2020)
Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference. CoRR (2020)
Javier Antorán, Antonio Miguel
Disentangling in Variational Autoencoders with Natural Clustering. CoRR (2019)
Javier Antorán, Antonio Miguel
Disentangling and Learning Robust Representations with Natural Clustering. ICMLA (2019)