Antoran, Javier

17 publications

NeurIPS 2024 A Generative Model of Symmetry Transformations James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric Nalisnick, José Miguel Hernández-Lobato

TMLR 2024 Deep End-to-End Causal Inference Tomas Geffner, Javier Antoran, 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

TMLR 2024 Image Reconstruction via Deep Image Prior Subspaces Riccardo Barbano, Javier Antoran, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin, Zeljko Kereta

NeurIPS 2024 Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, Javier Antorán, José Miguel Hernández-Lobato

ICLR 2024 Stochastic Gradient Descent for Gaussian Processes Done Right Jihao Andreas Lin, Shreyas Padhy, Javier Antoran, Austin Tripp, Alexander Terenin, Csaba Szepesvari, José Miguel Hernández-Lobato, David Janz

NeurIPS 2023 SE(3) Equivariant Augmented Coupling Flows Laurence Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato

NeurIPSW 2023 SE(3) Equivariant Augmented Coupling Flows Laurence Illing Midgley, Vincent Stimper, Javier Antoran, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato

NeurIPS 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

ICLR 2023 Sampling-Based Inference for Large Linear Models, with Application to Linearised Laplace Javier Antoran, Shreyas Padhy, Riccardo Barbano, Eric Nalisnick, David Janz, José Miguel Hernández-Lobato

TMLR 2023 Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior Javier Antoran, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin

ICML 2022 Adapting the Linearised Laplace Model Evidence for Modern Deep Learning Javier Antoran, David Janz, James U Allingham, Erik Daxberger, Riccardo Rb Barbano, Eric Nalisnick, Jose Miguel Hernandez-Lobato

NeurIPSW 2022 Deep End-to-End Causal Inference Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Agrin Hilmkil, Joel Jennings, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang

NeurIPSW 2022 Learning Generative Models with Invariance to Symmetries James Urquhart Allingham, Javier Antoran, Shreyas Padhy, Eric Nalisnick, José Miguel Hernández-Lobato

NeurIPSW 2021 Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not Chelsea Murray, James Urquhart Allingham, Javier Antoran, José Miguel Hernández-Lobato

ICML 2021 Bayesian Deep Learning via Subnetwork Inference Erik Daxberger, Eric Nalisnick, James U Allingham, Javier Antoran, Jose Miguel Hernandez-Lobato

ICLR 2021 Getting a CLUE: A Method for Explaining Uncertainty Estimates Javier Antoran, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato

NeurIPS 2020 Depth Uncertainty in Neural Networks Javier Antoran, James Allingham, José Miguel Hernández-Lobato