Javier Antorán

41 papers A* 10C 2Journal 25Unranked 1

Year	Rank	Type	Title / Venue / Authors
2024	A*	`conf`	A Generative Model of Symmetry Transformations. NeurIPS James Urquhart Allingham, Bruno Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato
2024	J	`jnl`	A Generative Model of Symmetry Transformations. CoRR James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato
2024	J	`jnl`	Deep End-to-end Causal Inference. Trans. Mach. Learn. Res. 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
2024	J	`jnl`	Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models. CoRR Fengzhe Zhang, Jiajun He, Laurence I. Midgley, Javier Antorán, José Miguel Hernández-Lobato
2024	J	`jnl`	Image Reconstruction via Deep Image Prior Subspaces. Trans. Mach. Learn. Res. Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin, Zeljko Kereta
2024	A*	`conf`	Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes. NeurIPS Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, Javier Antorán, José Miguel Hernández-Lobato
2024	J	`jnl`	Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes. CoRR Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, Javier Antorán, José Miguel Hernández-Lobato
2024	J	`jnl`	Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks. CoRR Javier Antorán
2024	A*	`conf`	Stochastic Gradient Descent for Gaussian Processes Done Right. ICLR Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
2024	—	`ed.`	Symposium on Advances in Approximate Bayesian Inference, Vienna, Austria, 21 July 2024. AABI Javier Antorán, Christian A. Naesseth
2023	J	`jnl`	Fast and Painless Image Reconstruction in Deep Image Prior Subspaces. CoRR Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Zeljko Kereta, Bangti Jin
2023	J	`jnl`	Online Laplace Model Selection Revisited. CoRR Jihao Andreas Lin, Javier Antorán, José Miguel Hernández-Lobato
2023	—	`ed.`	Proceedings on "I Can't Believe It's Not Better: Failure Modes in the Age of Foundation Models" at NeurIPS 2023 Workshops, 16 December 2023, New Orleans, Louisiana, USA. ICBINB Javier Antorán, Arno Blaas, Kelly Buchanan, Fan Feng, Vincent Fortuin, Sahra Ghalebikesabi, Andreas Kriegler, Ian Mason, David Rohde, Francisco J. R. Ruiz, Tobias Uelwer, Yubin Xie, Rui Yang
2023	A*	`conf`	SE(3) Equivariant Augmented Coupling Flows. NeurIPS Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato
2023	J	`jnl`	SE(3) Equivariant Augmented Coupling Flows. CoRR Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato
2023	A*	`conf`	Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. NeurIPS Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin
2023	J	`jnl`	Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. CoRR Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin
2023	A*	`conf`	Sampling-based inference for large linear models, with application to linearised Laplace. ICLR Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato
2023	J	`jnl`	Stochastic Gradient Descent for Gaussian Processes Done Right. CoRR Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
2023	J	`jnl`	Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior. Trans. Mach. Learn. Res. Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin
2022	J	`jnl`	A Probabilistic Deep Image Prior for Computational Tomography. CoRR Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin
2022	A*	`conf`	Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. ICML Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
2022	J	`jnl`	Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. CoRR Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
2022	J	`jnl`	Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior. CoRR Riccardo Barbano, Johannes Leuschner, Javier Antorán, Bangti Jin, José Miguel Hernández-Lobato
2022	J	`jnl`	Deep End-to-end Causal Inference. CoRR Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
2022	—	`ed.`	Proceedings on "I Can't Believe It's Not Better! - Understanding Deep Learning Through Empirical Falsification" at NeurIPS 2022 Workshops, 03 December 2022, New Orleans, Louisiana, USA ICBINB Javier Antorán, Arno Blaas, Fan Feng, Sahra Ghalebikesabi, Ian Mason, Melanie F. Pradier, David Rohde, Francisco J. R. Ruiz, Aaron Schein
2022	J	`jnl`	Sampling-based inference for large linear models, with application to linearised Laplace. CoRR Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato
2021	—	`conf`	Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not. ICBINB@NeurIPS Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
2021	J	`jnl`	Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not. CoRR Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
2021	A*	`conf`	Bayesian Deep Learning via Subnetwork Inference. ICML Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
2021	J	`jnl`	Depth Uncertainty Networks for Active Learning. CoRR Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
2021	A*	`conf`	Getting a CLUE: A Method for Explaining Uncertainty Estimates. ICLR Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato
2021	C	`conf`	Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty. AIES 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
2020	A*	`conf`	Depth Uncertainty in Neural Networks. NeurIPS Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato
2020	J	`jnl`	Depth Uncertainty in Neural Networks. CoRR Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato
2020	J	`jnl`	Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference. CoRR Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
2020	J	`jnl`	Getting a CLUE: A Method for Explaining Uncertainty Estimates. CoRR Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato
2020	J	`jnl`	Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty. CoRR 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
2020	J	`jnl`	Variational Depth Search in ResNets. CoRR Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato
2019	C	`conf`	Disentangling and Learning Robust Representations with Natural Clustering. ICMLA Javier Antorán, Antonio Miguel
2019	J	`jnl`	Disentangling in Variational Autoencoders with Natural Clustering. CoRR Javier Antorán, Antonio Miguel