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Both uncertainty estimation and interpretability are important factors for trustworthy machine learning systems.
A practical Bayesian framework for backpropagation networks
David J. C. MacKay · 1992
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LSAC national longitudinal bar passage study
L.F. Wightman, H. Ramsey, and Law School Admission Council · 1998
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Statistical comparisons of classifiers over multiple data sets
Janez Demšar · 2006
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MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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Estimating or propagating gradients through stochastic neurons
Yoshua Bengio · 2013
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Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Probabilistic backpropagation for scalable learning of Bayesian neural networks
José Miguel Hernández-Lobato and Ryan P. Adams · 2015
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Uncertainty in Deep Learning
Yarin Gal · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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"why should i trust you?": Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Bayesian optimization with robust bayesian neural networks
Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, and Frank Hutter · 2016
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Real time image saliency for black box classifiers
Piotr Dabkowski and Yarin Gal · 2017
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Sensitivity analysis for predictive uncertainty
Stefan Depeweg, José Miguel Hernández-Lobato, Steffen Udluft, and Thomas A. Runkler · 2017
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Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez and Been Kim · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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From parity to preference-based notions of fairness in classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Rodriguez, Krishna Gummadi, and Adrian Weller · 2017
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Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning
Stefan Depeweg, Jose-Miguel Hernandez-Lobato, Finale Doshi-Velez, and Steffen Udluft · 2018
Cited alongside, same era.
Explanations based on the missing: Towards contrastive explanations with pertinent negatives
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das · 2018
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Bayesian variational autoencoders for unsupervised out-of-distribution detection, 2019
Erik Daxberger and José Miguel Hernández-Lobato · 2019
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Modeling Epistemic and Aleatoric Uncertainty with Bayesian Neural Networks and Latent Variables
Stefan Depeweg · 2019
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Variational autoencoder with arbitrary conditioning
Oleg Ivanov, Michael Figurnov, and Dmitry Vetrov · 2019
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The seven tools of causal inference, with reflections on machine learning
Judea Pearl · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Efficient search for diverse coherent explanations
Chris Russell · 2019
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Julia Dressel and Hany Farid · 2018
Cited alongside, same era.
Human perceptions of fairness in algorithmic decision making: A case study of criminal risk prediction
Nina Grgic-Hlaca, Elissa M Redmiles, Krishna P Gummadi, and Adrian Weller · 2018
Cited alongside, same era.
Metrics for explainable ai: Challenges and prospects
Robert R Hoffman, Shane T Mueller, Gary Klein, and Jordan Litman · 2018
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xGEMs: Generating examplars to explain black-box models
Shalmali Joshi, Oluwasanmi Koyejo, Been Kim, and Joydeep Ghosh · 2018
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Variational continual learning
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, and Richard E. Turner · 2018
Cited alongside, same era.
Distribution Matching in Variational Inference
Mihaela Rosca, Balaji Lakshminarayanan, and Shakir Mohamed · 2018
Cited alongside, same era.
Counterfactual explanations without opening the black box: Automated decisions and the GDPR
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2018
Cited alongside, same era.
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Actionable recourse in linear classification
Berk Ustun, Alexander Spangher, and Yang Liu · 2019
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Transparency: Motivations and challenges
Adrian Weller · 2019
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Debugging tests for model explanations
Julius Adebayo, Michael Muelly, Ilaria Liccardi, and Been Kim · 2020
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Evaluating and aggregating feature-based model explanations
Umang Bhatt, Adrian Weller, and José M. F. Moura · 2020
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Explainable machine learning in deployment
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, and Peter Eckersley · 2020
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Bayes-probe: Distribution-guided sampling for prediction level sets
Serena Booth, Yilun Zhou, Ankit Shah, and Julie Shah · 2020
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On the variance of the adaptive learning rate and beyond
Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han · 2020
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Certifai: A common framework to provide explanations and analyse the fairness and robustness of black-box models
Shubham Sharma, Jette Henderson, and Joydeep Ghosh · 2020
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Sample-efficient optimization in the latent space of deep generative models via weighted retraining
Austin Tripp, Erik Daxberger, and José Miguel Hernández-Lobato · 2020
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Bayesian deep learning and a probabilistic perspective of generalization, 2020
Andrew Gordon Wilson and Pavel Izmailov · 2020
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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, Madhulika Srikumar, Adrian Weller, and Alice Xiang · 2021
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