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Quantifying Uncertainty for Machine Learning Based Diagnostic


Jul 29, 2021
Owen Convery, Lewis Smith, Yarin Gal, Adi Hanuka

* arXiv admin note: substantial text overlap with arXiv:2105.04654 

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Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks


Jul 23, 2021
Andrey Malinin, Neil Band, Ganshin, Alexander, German Chesnokov, Yarin Gal, Mark J. F. Gales, Alexey Noskov, Andrey Ploskonosov, Liudmila Prokhorenkova, Ivan Provilkov, Vatsal Raina, Vyas Raina, Roginskiy, Denis, Mariya Shmatova, Panos Tigas, Boris Yangel


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Prioritized training on points that are learnable, worth learning, and not yet learned


Jul 06, 2021
Sören Mindermann, Muhammed Razzak, Winnie Xu, Andreas Kirsch, Mrinank Sharma, Adrien Morisot, Aidan N. Gomez, Sebastian Farquhar, Jan Brauner, Yarin Gal

* ICML 2021 Workshop on Subset Selection in Machine Learning 

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Improving black-box optimization in VAE latent space using decoder uncertainty


Jun 30, 2021
Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal


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A Practical & Unified Notation for Information-Theoretic Quantities in ML


Jun 22, 2021
Andreas Kirsch, Yarin Gal


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A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions


Jun 22, 2021
Andreas Kirsch, Sebastian Farquhar, Yarin Gal


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Active Learning under Pool Set Distribution Shift and Noisy Data


Jun 22, 2021
Andreas Kirsch, Tom Rainforth, Yarin Gal


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Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective


Jun 17, 2021
Lewis Smith, Joost van Amersfoort, Haiwen Huang, Stephen Roberts, Yarin Gal

* Main paper 10 pages including references, appendix 10 pages. 7 figures and 6 tables including appendix 

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KL Guided Domain Adaptation


Jun 14, 2021
A. Tuan Nguyen, Toan Tran, Yarin Gal, Philip H. S. Torr, Atılım Güneş Baydin


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Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning


Jun 07, 2021
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran


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Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning


Jun 04, 2021
Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Tom Rainforth, Yarin Gal

* First two authors contributed equally 

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Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization


May 05, 2021
Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa, Farrukh Chishtie, Natalia Díaz-Rodríguez, Océane Boulais, Aruna Sankaranarayanan, Aaron Piña, Yarin Gal, Chedy Raïssi, Alexander Lavin, Dava Newman

* arXiv admin note: text overlap with arXiv:2010.08103 

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Outcome-Driven Reinforcement Learning via Variational Inference


Apr 20, 2021
Tim G. J. Rudner, Vitchyr H. Pong, Rowan McAllister, Yarin Gal, Sergey Levine


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Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties


Mar 16, 2021
Lisa Schut, Oscar Key, Rory McGrath, Luca Costabello, Bogdan Sacaleanu, Medb Corcoran, Yarin Gal

* Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021 
* 21 pages, 13 Figures 

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Robustness to Pruning Predicts Generalization in Deep Neural Networks


Mar 10, 2021
Lorenz Kuhn, Clare Lyle, Aidan N. Gomez, Jonas Rothfuss, Yarin Gal


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Active Testing: Sample-Efficient Model Evaluation


Mar 09, 2021
Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth


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Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding


Mar 08, 2021
Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit

* 18 pages, 5 figures, In review 

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PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning


Feb 24, 2021
Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar


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Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty


Feb 23, 2021
Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal


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Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression


Feb 22, 2021
Joost van Amersfoort, Lewis Smith, Andrew Jesson, Oscar Key, Yarin Gal


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Domain Invariant Representation Learning with Domain Density Transformations


Feb 14, 2021
A. Tuan Nguyen, Toan Tran, Yarin Gal, Atılım Güneş Baydin


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Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition


Feb 02, 2021
Panagiotis Tigas, Téo Bloch, Vishal Upendran, Banafsheh Ferdoushi, Mark C. M. Cheung, Siddha Ganju, Ryan M. McGranaghan, Yarin Gal, Asti Bhatt

* Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada 

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Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning


Feb 01, 2021
Luiz F. G. dos Santos, Souvik Bose, Valentina Salvatelli, Brad Neuberg, Mark C. M. Cheung, Miho Janvier, Meng Jin, Yarin Gal, Paul Boerner, Atılım Güneş Baydin

* 12 pages, 7 figures, 8 tables. This is a pre-print of an article submitted and accepted by A&A Journal 

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On Statistical Bias In Active Learning: How and When To Fix It


Jan 27, 2021
Sebastian Farquhar, Yarin Gal, Tom Rainforth

* Published at ICLR 2021 (Spotlight) 

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Technology Readiness Levels for Machine Learning Systems


Jan 11, 2021
Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic, Siddha Ganju, Dava Newman, Sujoy Ganguly, Danny Lange, Atılım Güneş Baydin, Amit Sharma, Adam Gibson, Yarin Gal, Eric P. Xing, Chris Mattmann, James Parr


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