Javier Antorán

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All published works

Action	Title	Year	Authors
+ PDF Chat	Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models	2024	Z. W. Fan Jiajun He Laurence I. Midgley Javier Antorán José Miguel Hernández-Lobato
+ PDF Chat	Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes	2024	Jihao Andreas Lin Shreyas Padhy Bruno Mlodozeniec Javier Antorán José Miguel Hernández-Lobato
+ PDF Chat	Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks	2024	Javier Antorán
+ PDF Chat	A Generative Model of Symmetry Transformations	2024	James Urquhart Allingham Bruno Mlodozeniec Shreyas Padhy Javier Antorán David Krueger Richard E. Turner Eric Nalisnick José Miguel Hernández-Lobato
+	Image Reconstruction via Deep Image Prior Subspaces	2023	Riccardo Barbano Javier Antorán Johannes Leuschner José Miguel Hernández-Lobato Željko Kereta Bangti Jin
+	Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent	2023	Jihao Andreas Lin Javier Antorán Shreyas Padhy David M. Janz José Miguel Hernández-Lobato Alexander Terenin
+	Online Laplace Model Selection Revisited	2023	Jihao Andreas Lin Javier Antorán José Miguel Hernández-Lobato
+	SE(3) Equivariant Augmented Coupling Flows	2023	Laurence I. Midgley Vincent Stimper Javier Antorán Émile Mathieu Bernhard Schölkopf José Miguel Hernández-Lobato
+	Stochastic Gradient Descent for Gaussian Processes Done Right	2023	Jihao Andreas Lin Shreyas Padhy Javier Antorán Austin Tripp Alexander Terenin Csaba Szepesvári José Miguel Hernández-Lobato David M. Janz
+	Deep End-to-end Causal Inference	2022	Tomas Geffner Javier Antorán Adam S. Foster Wenbo Gong Chao Ma Emre Kıcıman Amit Sharma Angus Lamb Martin Kukla Nick Pawlowski
+	Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior	2022	Javier Antorán Riccardo Barbano Johannes Leuschner José Miguel Hernández-Lobato Bangti Jin
+	Adapting the Linearised Laplace Model Evidence for Modern Deep Learning	2022	Javier Antorán David M. Janz James Urquhart Allingham Erik Daxberger Riccardo Barbano Eric Nalisnick José Miguel Hernández-Lobato
+	Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior	2022	Riccardo Barbano Johannes Leuschner Javier Antorán Bangti Jin José Miguel Hernández-Lobato
+	Sampling-based inference for large linear models, with application to linearised Laplace	2022	Javier Antorán Shreyas Padhy Riccardo Barbano Eric Nalisnick David M. Janz José Miguel Hernández-Lobato
+ PDF Chat	Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not	2021	Chelsea Murray James Urquhart Allingham Javier Antorán José Miguel Hernández-Lobato
+	Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty	2021	Umang Bhatt Javier Antorán Yunfeng Zhang Q. Vera Liao Prasanna Sattigeri Riccardo Fogliato Gabrielle Gauthier Melançon Ranganath Krishnan Jason Stanley Omesh Tickoo
+	Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not	2021	Chelsea Murray James Urquhart Allingham Javier Antorán José Miguel Hernández-Lobato
+	Depth Uncertainty Networks for Active Learning	2021	Chelsea Murray James Urquhart Allingham Javier Antorán José Miguel Hernández-Lobato
+	Depth Uncertainty in Neural Networks	2020	Javier Antorán James Urquhart Allingham José Miguel Hernández-Lobato
+	Variational Depth Search in ResNets.	2020	Javier Antorán James Urquhart Allingham José Miguel Hernández-Lobato
+	Getting a CLUE: A Method for Explaining Uncertainty Estimates	2020	Javier Antorán Umang Bhatt Tameem Adel Adrian Weller José Miguel Hernández-Lobato
+	Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty	2020	Umang Bhatt Yunfeng Zhang Javier Antorán Q. Vera Liao Prasanna Sattigeri Riccardo Fogliato Gabrielle Gauthier Melançon Ranganath Krishnan Jason Stanley Omesh Tickoo
+	Bayesian Deep Learning via Subnetwork Inference	2020	Erik Daxberger Eric Nalisnick James Urquhart Allingham Javier Antorán José Miguel Hernández-Lobato
+	Depth Uncertainty in Neural Networks	2020	Javier Antorán James Urquhart Allingham José Miguel Hernández-Lobato
+	Variational Depth Search in ResNets	2020	Javier Antorán James Urquhart Allingham José Miguel Hernández-Lobato
+ PDF Chat	Disentangling and Learning Robust Representations with Natural Clustering	2019	Javier Antorán Antonio Miguel
+	Disentangling in Variational Autoencoders with Natural Clustering.	2019	Javier Antorán Antonio Miguel

Common Coauthors

Coauthor	Papers Together
José Miguel Hernández-Lobato	21
James Urquhart Allingham	10
Riccardo Barbano	5
Shreyas Padhy	5
Jihao Andreas Lin	4
David M. Janz	4
Eric Nalisnick	4
Adrian Weller	3
Umang Bhatt	3
Johannes Leuschner	3
Bangti Jin	3
Omesh Tickoo	2
Rumi Chunara	2
Riccardo Fogliato	2
Q. Vera Liao	2
Lama Nachman	2
Ranganath Krishnan	2
Alice Xiang	2
Alexander Terenin	2
Chelsea Murray	2
Laurence I. Midgley	2
Bruno Mlodozeniec	2
Prasanna Sattigeri	2
Gabrielle Gauthier Melançon	2
Erik Daxberger	2
Yunfeng Zhang	2
Antonio Miguel	2
Jason Stanley	2
Wenbo Gong	1
Adam S. Foster	1
Vincent Stimper	1
Csaba Szepesvári	1
Richard E. Turner	1
Chao Ma	1
Željko Kereta	1
David Krueger	1
Miltiadis Allamanis	1
Angus Lamb	1
Madhulika Srikumar	1
Chelsea Murray	1
Tomas Geffner	1
Austin Tripp	1
Z. W. Fan	1
Martin Kukla	1
Nick Pawlowski	1
Emre Kıcıman	1
Bernhard Schölkopf	1
Cheng Zhang	1
Émile Mathieu	1
Tameem Adel	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Towards A Rigorous Science of Interpretable Machine Learning	2017	Finale Doshi‐Velez Been Kim	3
+	Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning	2015	Yarin Gal Zoubin Ghahramani	3
+	Explainable machine learning in deployment	2020	Umang Bhatt Alice Xiang Shubham Sharma Adrian Weller Ankur Taly Yunhan Jia Joydeep Ghosh Ruchir Puri José M. F. Moura Peter Eckersley	3
+	Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles	2016	Balaji Lakshminarayanan Alexander Pritzel Charles Blundell	3
+	Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms	2017	Xiao Han Kashif Rasul Roland Vollgraf	3
+	Violin Plots: A Box Plot-Density Trace Synergism	1998	Jerry L. Hintze Ray D. Nelson	2
+ PDF Chat	Model Cards for Model Reporting	2019	Margaret Mitchell Simone Wu Andrew Zaldivar Parker Barnes Lucy Vasserman Ben Hutchinson Elena Spitzer Inioluwa Deborah Raji Timnit Gebru	2
+ PDF Chat	Visual Reasoning Strategies for Effect Size Judgments and Decisions	2020	Alex Kale Matthew Kay Jessica Hullman	2
+ PDF Chat	Frequentist and Bayesian approaches to data analysis: Evaluation and estimation	2019	Jolynn Pek Trisha Van Zandt	2
+	Practical Deep Learning with Bayesian Principles	2019	Kazuki Osawa Siddharth Swaroop Mohammad Emtiyaz Khan Anirudh Jain Runa Eschenhagen Richard E. Turner Rio Yokota	2
+	Fairness Evaluation in Presence of Biased Noisy Labels	2020	Riccardo Fogliato Max G’Sell Alexandra Chouldechova	2
+ PDF Chat	Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making	2020	Yunfeng Zhang Q. Vera Liao Rachel Bellamy	2
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	2
+	Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift	2015	Sergey Ioffe Christian Szegedy	2
+	Bayesian Deep Learning and a Probabilistic Perspective of Generalization	2020	Andrew Gordon Wilson Pavel Izmailov	2
+	Stochastic Backpropagation and Approximate Inference in Deep Generative Models	2014	Danilo Jimenez Rezende Shakir Mohamed Daan Wierstra	2
+	Semi-Supervised Learning with Deep Generative Models	2014	Diederik P. Kingma Danilo Jimenez Rezende Shakir Mohamed Max Welling	2
+	Weight Uncertainty in Neural Networks	2015	Charles Blundell Julien Cornebise Koray Kavukcuoglu Daan Wierstra	2
+	Neural Discrete Representation Learning	2017	Aäron van den Oord Oriol Vinyals Koray Kavukcuoglu	2
+	Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift	2019	Yaniv Ovadia Emily Fertig Jie Ren Zachary Nado D. Sculley Sebastian Nowozin Joshua V. Dillon Balaji Lakshminarayanan Jasper Snoek	2
+	Conceptual difficulties when interpreting histograms: A review	2019	Lonneke Boels Arthur Bakker Wim Van Dooren Paul Drijvers	2
+ PDF Chat	FactSheets: Increasing trust in AI services through supplier's declarations of conformity	2019	Matthew Arnold Rachel Bellamy Michael Hind Stephanie Houde Sameep Mehta Aleksandra Mojsilović Ravi Nair Karthikeyan Natesan Ramamurthy Adriana Olteanu David Piorkowski	2
+	Functional Variational Bayesian Neural Networks	2019	Shengyang Sun Guodong Zhang Jiaxin Shi Roger Grosse	2
+ PDF Chat	Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved	2019	Jiahao Chen Nathan Kallus Xiaojie Mao Geoffry Svacha Madeleine Udell	2
+	Predictive Entropy Search for Efficient Global Optimization of Black-box Functions	2014	José Miguel Hernández-Lobato Matthew W. Hoffman Zoubin Ghahramani	2
+	Confidence Distribution, the Frequentist Distribution Estimator of a Parameter: A Review	2013	Minge Xie Kesar Singh	2
+	Disentangling by Factorising	2018	Hyunjik Kim Andriy Mnih	2
+ PDF Chat	Hypothetical Outcome Plots Outperform Error Bars and Violin Plots for Inferences about Reliability of Variable Ordering	2015	Jessica Hullman Paul Resnick Eytan Adar	2
+ PDF Chat	Identity Mappings in Deep Residual Networks	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	2
+	The Bagplot: A Bivariate Boxplot	1999	Peter J. Rousseeuw Ida Ruts John W. Tukey	2
+	Bayesian Active Learning for Classification and Preference Learning	2011	Neil Houlsby Ferenc Huszár Zoubin Ghahramani Máté Lengyel	2
+ PDF Chat	Theoretical Considerations and Development of a Questionnaire to Measure Trust in Automation	2018	Moritz Körber	2
+	Understanding disentangling in $β$-VAE	2018	Christopher Burgess Irina Higgins Arka Pal Löıc Matthey Nick Watters Guillaume Desjardins Alexander Lerchner	2
+	Recent Advances in Autoencoder-Based Representation Learning	2018	Michael Tschannen Olivier Bachem Mario Lučić	2
+	Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift	2015	Sergey Ioffe Christian Szegedy	2
+ PDF Chat	To Predict and Serve?	2016	Kristian Lum William Isaac	2
+	Conditional Generative Adversarial Nets	2014	Mehdi Mirza Simon Osindero	2
+	Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks	2017	Lars Mescheder Sebastian Nowozin Andreas Geiger	2
+	Variational continual learning	2017	Cuong V. Nguyen Yingzhen Li Thang D. Bui Richard E. Turner	2
+	Axiomatic Attribution for Deep Networks	2017	Mukund Sundararajan Ankur Taly Qiqi Yan	2
+	Bayesian Learning of Neural Network Architectures	2019	Georgi Dikov Justin Bayer	2
+ PDF Chat	The effects of communicating uncertainty on public trust in facts and numbers	2020	Anne Marthe van der Bles Sander van der Linden Alexandra L. J. Freeman David Spiegelhalter	2
+	A Simple Baseline for Bayesian Uncertainty in Deep Learning	2019	Wesley J. Maddox Pavel Izmailov Timur Garipov Dmitry Vetrov Andrew Gordon Wilson	2
+	Successor Uncertainties: exploration and uncertainty in temporal difference learning	2019	David M. Janz Jiri Hron P. Mazur Katja Hofmann José Miguel Hernández-Lobato Sebastian Tschiatschek	2
+	SCAN: Learning Abstract Hierarchical Compositional Visual Concepts	2017	Irina Higgins Nicolas Sonnerat Löıc Matthey Arka Pal Christopher Burgess Matthew Botvinick Demis Hassabis Alexander Lerchner	2
+ PDF Chat	Representation Learning: A Review and New Perspectives	2013	Yoshua Bengio Aaron Courville P. M. Durai Raj Vincent	2
+	Introduction to the Theory of Statistics, 3rd ed.	1974	Gordon V. Kass Alexander M. Mood Franklin A. Graybill Duane C. Boes	1
+	Improving predictive inference under covariate shift by weighting the log-likelihood function	2000	Hidetoshi Shimodaira	1
+	Overpruning in Variational Bayesian Neural Networks	2018	Brian L. Trippe Richard E. Turner	1
+	Bayesian Learning via Stochastic Gradient Langevin Dynamics	2011	Max Welling Yee Whye Teh	1