Getting a CLUE: A Method for Explaining Uncertainty Estimates

11 June 2020

Javier Antorán

Umang Bhatt

T. Adel

Adrian Weller

José Miguel Hernández-Lobato

UQCV

BDL

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Papers citing "Getting a CLUE: A Method for Explaining Uncertainty Estimates"

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Title
From Search To Sampling: Generative Models For Robust Algorithmic Recourse Prateek Garg Lokesh Nagalapatti Sunita Sarawagi 31 0 0 12 May 2025
Uncertainty Quantification for Machine Learning in Healthcare: A Survey L. J. L. Lopez Shaza Elsharief Dhiyaa Al Jorf Firas Darwish Congbo Ma Farah E. Shamout 98 0 0 04 May 2025
What is different between these datasets? Varun Babbar Zhicheng Guo Cynthia Rudin 59 1 0 08 Mar 2024
QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations J. Duell M. Seisenberger Hsuan-Wei Fu Xiuyi Fan UQCV BDL 34 1 0 27 Feb 2024
A Framework for Variational Inference of Lightweight Bayesian Neural Networks with Heteroscedastic Uncertainties D. Schodt Ryan Brown Michael Merritt Samuel Park Delsin Menolascino M. Peot BDL UQCV UD 24 1 0 22 Feb 2024
Identifying Drivers of Predictive Aleatoric Uncertainty Pascal Iversen Simon Witzke Katharina Baum Bernhard Y. Renard UD 43 1 0 12 Dec 2023
Endogenous Macrodynamics in Algorithmic Recourse Patrick Altmeyer Giovan Angela Aleksander Buszydlik Karol Dobiczek A. V. Deursen Cynthia C. S. Liem 21 7 0 16 Aug 2023
Explaining Black-Box Models through Counterfactuals Patrick Altmeyer A. V. Deursen Cynthia C. S. Liem CML LRM 34 2 0 14 Aug 2023
MoleCLUEs: Molecular Conformers Maximally In-Distribution for Predictive Models Michael R. Maser Natasa Tagasovska Jae Hyeon Lee Andrew Watkins 31 0 0 20 Jun 2023
Learning Personalized Decision Support Policies Umang Bhatt Valerie Chen Katherine M. Collins Parameswaran Kamalaruban Emma Kallina Adrian Weller Ameet Talwalkar OffRL 48 10 0 13 Apr 2023
CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers' Decisions Julia El Zini Mohamad Mansour M. Awad 19 1 0 19 Jan 2023
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis Yuxin Xiao Paul Pu Liang Umang Bhatt W. Neiswanger Ruslan Salakhutdinov Louis-Philippe Morency 175 86 0 10 Oct 2022
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning Javier Antorán David Janz J. Allingham Erik A. Daxberger Riccardo Barbano Eric T. Nalisnick José Miguel Hernández-Lobato UQCV BDL 25 28 0 17 Jun 2022
Gradient-based Counterfactual Explanations using Tractable Probabilistic Models Xiaoting Shao Kristian Kersting BDL 22 1 0 16 May 2022
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse Martin Pawelczyk Teresa Datta Johannes van-den-Heuvel Gjergji Kasneci Himabindu Lakkaraju 19 38 0 13 Mar 2022
McXai: Local model-agnostic explanation as two games Yiran Huang Nicole Schaal Michael Hefenbrock Yexu Zhou T. Riedel Likun Fang Michael Beigl FAtt 10 4 0 04 Jan 2022
Shapley variable importance clouds for interpretable machine learning Yilin Ning M. Ong Bibhas Chakraborty B. Goldstein Daniel Ting Roger Vaughan Nan Liu FAtt 21 69 0 06 Oct 2021
Deep Neural Networks and Tabular Data: A Survey V. Borisov Tobias Leemann Kathrin Seßler Johannes Haug Martin Pawelczyk Gjergji Kasneci LMTD 27 645 0 05 Oct 2021
A Survey of Uncertainty in Deep Neural Networks J. Gawlikowski Cedrique Rovile Njieutcheu Tassi Mohsin Ali Jongseo Lee Matthias Humt ... R. Roscher Muhammad Shahzad Wen Yang R. Bamler Xiaoxiang Zhu BDL UQCV OOD 30 1,109 0 07 Jul 2021
Explaining in Style: Training a GAN to explain a classifier in StyleSpace Oran Lang Yossi Gandelsman Michal Yarom Yoav Wald G. Elidan ... William T. Freeman Phillip Isola Amir Globerson Michal Irani Inbar Mosseri GAN 35 152 0 27 Apr 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 53 651 0 20 Mar 2021
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,235 0 24 Jun 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,681 0 28 Feb 2017