Disentangling and Learning Robust Representations with Natural Clustering. - Javier Antorán; Antonio Miguel (ICMLA, 2019)

Disentangling in Variational Autoencoders with Natural Clustering. - Javier Antorán; Antonio Miguel (CoRR, 2019)

Depth Uncertainty in Neural Networks. - Javier Antorán; James Urquhart Allingham; José Miguel Hernández-Lobato (NeurIPS, 2020)

Variational Depth Search in ResNets. - Javier Antorán; James Urquhart Allingham; José Miguel Hernández-Lobato (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)

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; et al. (CoRR, 2020)

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; et al. (AIES, 2021)

Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not. - Chelsea Murray; James Urquhart Allingham; Javier Antorán; José Miguel Hernández-Lobato (ICBINB@NeurIPS, 2021)

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

Bayesian Deep Learning via Subnetwork Inference. - Erik A. Daxberger; Eric T. Nalisnick; James Urquhart Allingham; Javier Antorán; José Miguel Hernández-Lobato (ICML, 2021)

Depth Uncertainty Networks for Active Learning. - Chelsea Murray; James Urquhart Allingham; Javier Antorán; José Miguel Hernández-Lobato (CoRR, 2021)

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 (ICML, 2022)

Deep End-to-end Causal Inference. - Tomas Geffner; Javier Antorán; Adam Foster 001; Wenbo Gong 001; Chao Ma 019; Emre Kiciman; Amit Sharma 007; Angus Lamb; Martin Kukla; Nick Pawlowski; Miltiadis Allamanis; Cheng Zhang 005 (CoRR, 2022)

A Probabilistic Deep Image Prior for Computational Tomography. - Javier Antorán; Riccardo Barbano; Johannes Leuschner; José Miguel Hernández-Lobato; Bangti Jin (CoRR, 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)

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 (ICLR, 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 (NeurIPS, 2023)

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

Online Laplace Model Selection Revisited. - Jihao Andreas Lin; Javier Antorán; José Miguel Hernández-Lobato (CoRR, 2023)

Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior. - Javier Antorán; Riccardo Barbano; Johannes Leuschner; José Miguel Hernández-Lobato; Bangti Jin (Trans. Mach. Learn. Res., 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 (ICLR, 2024)

A Generative Model of Symmetry Transformations. - James Urquhart Allingham; Bruno Mlodozeniec; Shreyas Padhy; Javier Antorán; David Krueger 001; Richard E. Turner; Eric T. Nalisnick; José Miguel Hernández-Lobato (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 (NeurIPS, 2024)

Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks. - Javier Antorán (CoRR, 2024)

Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models. - Fengzhe Zhang; Jiajun He 003; Laurence I. Midgley; Javier Antorán; José Miguel Hernández-Lobato (CoRR, 2024)

Image Reconstruction via Deep Image Prior Subspaces. - Riccardo Barbano; Javier Antorán; Johannes Leuschner; José Miguel Hernández-Lobato; Bangti Jin; Zeljko Kereta (Trans. Mach. Learn. Res., 2024)

Deep End-to-end Causal Inference. - Tomas Geffner; Javier Antorán; Adam Foster 001; Wenbo Gong 001; Chao Ma 019; Emre Kiciman; Amit Sharma 007; Angus Lamb; Martin Kukla; Nick Pawlowski; Agrin Hilmkil; Joel Jennings; et al. (Trans. Mach. Learn. Res., 2024)