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Privacy and transparency are two key foundations of trustworthy machine learning.
The many Shapley values for model explanation
Sundararajan, M.; and Najmi, A. 2019 · 1908
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Problems with Shapley-value-based explanations as feature importance measures
Kumar, I. E.; Venkatasubramanian, S.; Scheidegger, C.; and Friedler, S. 2020 · 2002
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Laplacian eigenmaps for dimensionality reduction and data representation
Belkin, M.; and Niyogi, P. 2003 · 2003
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Charting a manifold
Brand, M. 2003 · 2003
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SNPing away at anonymity
de Souza, N. 2008 · 2008
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Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays
Homer, N.; Szelinger, S.; Redman, M.; Duggan, D.; Tembe, W.; Muehling, J.; Pearson, J. V.; Stephan, D. A.; Nelson, S. F.; and Craig, D. W. 2008 · 2008
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How to Explain Individual Classification Decisions
Baehrens, D.; Schroeter, T.; Harmeling, S.; Kawanabe, M.; Hansen, K.; and Mueller, K. 2009 · 2009
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On Finding Directed Trees with Many Leaves
Daligault, J.; and Thomassé, S. 2009 · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A.; and Hinton, G. 2009 · 2009
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Sample complexity of testing the manifold hypothesis
Narayanan, H.; and Mitter, S. 2010 · 2010
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The Right to Information and Privacy: Balancing Rights and Managing Conflicts
Banisar, D. 2011 · 2011
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Kerckhoffs’ Principle
Petitcolas, F. 2011 · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K.; Vedaldi, A.; and Zisserman, A. 2013 · 2013
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Striving for Simplicity: The All Convolutional Net
Springenberg, J. T.; Dosovitskiy, A.; Brox, T.; and Riedmiller, M. 2014 · 2014
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Impact of HbA1c measurement on hospital readmission rates: Analysis of 70,000 clinical database patient records
Strack, B.; Deshazo, J. P.; Gennings, C.; Olmo, J. L.; Ventura, S.; Cios, K. J.; and Clore, J. N. 2014 · 2014
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Influence in Classification via Cooperative Game Theory
Datta, A.; Datta, A.; Procaccia, A. D.; and Zick, Y. 2015 · 2015
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On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Klauschen, F.; Müller, K.; Binder, A.; Montavon, G.; Samek, W.; and Bach, S. 2015 · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; Berg, A. C.; and Fei-Fei, L. 2015 · 2015
Cited alongside, same era.
Deep learning with differential privacy
Abadi, M.; Chu, A.; Goodfellow, I.; McMahan, H. B.; Mironov, I.; Talwar, K.; and Zhang, L. 2016 · 2016
Cited alongside, same era.
Transparency via Quantitative Input Influence
Datta, A.; Sen, S.; and Zick, Y. 2016 · 2016
Cited alongside, same era.
Testing the manifold hypothesis
Fefferman, C.; and Mitter, Sand Narayanan, H. 2016 · 2016
Cited alongside, same era.
Why Should I Trust You?: Explaining the Predictions of Any Classifier
Ribeiro, M. T.; Singh, S.; and Guestrin, C. 2016 · 2016
Towards better understanding of gradient-based attribution methods for Deep Neural Networks
Ancona, M.; Ceolini, E.; Öztireli, C.; and Gross, M. 2018 · 2018
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The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets
Carlini, N.; Liu, C.; Kos, J.; Erlingsson, Ú.; and Song, D. 2018 · 2018
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Gilmer, J.; Metz, L.; Faghri, F.; Schoenholz, S. S.; Raghu, M.; Wattenberg, M.; and Goodfellow, I. 2018 · 2018
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Machine Learning with Membership Privacy using Adversarial Regularization
Nasr, M.; Shokri, R.; and Houmansadr, A. 2018 · 2018
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Scalable Private Learning with PATE
Papernot, N.; Song, S.; Mironov, I.; Raghunathan, A.; Talwar, K.; and Erlingsson, Ú. 2018 · 2018
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Cited alongside, same era.
Programs as Black-Box Explanations
Singh, S.; Ribeiro, M. T.; and Guestrin, C. 2016 · 2016
Cited alongside, same era.
Use Privacy in Data-Driven Systems: Theory and Experiments with Machine Learnt Programs
Datta, A.; Fredrikson, M.; Ko, G.; Mardziel, P.; and Sen, S. 2017 · 2017
Cited alongside, same era.
UCI Machine Learning Repository
Dua, D.; and Graff, C. 2017 · 2017
Cited alongside, same era.
Exposed! a survey of attacks on private data
Dwork, C.; Smith, A.; Steinke, T.; and Ullman, J. 2017 · 2017
Cited alongside, same era.
European Union Regulations on Algorithmic Decision-Making and a “Right to Explanation”
Goodman, B.; and Flaxman, S. R. 2017 · 2017
Cited alongside, same era.
Understanding Black-box Predictions via Influence Functions
Koh, P. W.; and Liang, P. 2017 · 2017
Cited alongside, same era.
Anchors: High-Precision Model-Agnostic Explanations
Ribeiro, M. T.; Singh, S.; and Guestrin, C. 2018 · 2018
Later among the works it cites.
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
Yeom, S.; Giacomelli, I.; Fredrikson, M.; and Jha, S. 2018 · 2018
Later among the works it cites.
Gradient-Based Attribution Method
Ancona, M.; Ceolini, E.; Öztireli, C.; and Gross, M. 2019 · 2019
Closest in time.
Model Reconstruction from Model Explanations
Milli, S.; Schmidt, L.; Dragan, A. D.; and Hardt, M. 2019 · 2019
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White-box vs Black-box: Bayes Optimal Strategies for Membership Inference
Sablayrolles, A.; Douze, M.; Ollivier, Y.; Schmid, C.; and Jégou, H. 2019 · 2019
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Axiomatic Characterization of Data-Driven Influence Measures for Classification
Sliwinski, J.; Strobel, M.; and Zick, Y. 2019 · 2019
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Privacy risks of securing machine learning models against adversarial examples
Song, L.; Shokri, R.; and Mittal, P. 2019 · 2019
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Model extraction from counterfactual explanations
Aïvodji, U.; Bolot, A.; and Gambs, S. 2020 · 2020
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Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
Kaur, H.; Nori, H.; Jenkins, S.; Caruana, R.; Wallach, H.; and Wortman Vaughan, J. 2020 · 2020
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Guidance on the AI auditing framework Draft guidance for consultation
Office, I. C. 2020 · 2020
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Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods
Slack, D.; Hilgard, S.; Jia, E.; Singh, S.; and Lakkaraju, H. 2020 · 2020
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