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参考文献

“Definition of Algorithm.” https://www.merriam-webster.com/dictionary/algorithm. (2017).

Aamodt, Agnar, and Enric Plaza. “Case-based reasoning: Foundational issues, methodological variations, and system approaches.” AI communications 7.1 (1994): 39-59.

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Adebayo, Julius, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. “Sanity checks for saliency maps.” arXiv preprint arXiv:1810.03292 (2018).

Alain, Guillaume, and Yoshua Bengio. “Understanding intermediate layers using linear classifier probes.” arXiv preprint arXiv:1610.01644 (2016).

Alber, Maximilian, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, and Pieter-Jan Kindermans. “iNNvestigate neural networks!.” J. Mach. Learn. Res. 20, no. 93 (2019): 1-8.

Alberto, Túlio C, Johannes V Lochter, and Tiago A Almeida. “Tubespam: comment spam filtering on YouTube.” In Machine Learning and Applications (Icmla), Ieee 14th International Conference on, 138–43. IEEE. (2015).

Alvarez-Melis, David, and Tommi S. Jaakkola. “On the robustness of interpretability methods.” arXiv preprint arXiv:1806.08049 (2018).

Ancona, Marco, et al. “Towards better understanding of gradient-based attribution methods for deep neural networks.” arXiv preprint arXiv:1711.06104 (2017).

Apley, Daniel W., and Jingyu Zhu. “Visualizing the effects of predictor variables in black box supervised learning models.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82.4 (2020): 1059-1086.

Athalye, Anish, and Ilya Sutskever. “Synthesizing robust adversarial examples.” arXiv preprint arXiv:1707.07397 (2017).

Bach, Sebastian, et al. “On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation.” PloS one 10.7 (2015).

Bau, David, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba. “Network dissection: Quantifying interpretability of deep visual representations.” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6541-6549 (2017).

Biggio, Battista, and Fabio Roli. “Wild Patterns: Ten years after the rise of adversarial machine learning.” Pattern Recognition 84 (2018): 317-331.

Borgelt, C. “An implementation of the FP-growth algorithm.” Proceedings of the 1st International Workshop on Open Source Data Mining Frequent Pattern Mining Implementations - OSDM ’05, 1–5. http://doi.org/10.1145/1133905.1133907 (2005).

Breiman, Leo.“Random Forests.” Machine Learning 45 (1). Springer: 5-32 (2001).

Brown, Tom B., Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer. “Adversarial patch.” arXiv preprint arXiv:1712.09665 (2017).

Caruana, Rich, et al. “Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission.” Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. (2015).

Chen, Zhi, Yijie Bei, and Cynthia Rudin. “Concept whitening for interpretable image recognition.” Nature Machine Intelligence 2, no. 12 (2020): 772-782.

Cohen, William W. “Fast effective rule induction.” Machine Learning Proceedings (1995). 115-123.

Cook, R. Dennis. “Detection of influential observation in linear regression.” Technometrics 19.1 (1977): 15-18.

Dandl, Susanne, Christoph Molnar, Martin Binder, Bernd Bischl. “Multi-objective counterfactual explanations”. In: Bäck T. et al. (eds) Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. Lecture Notes in Computer Science, vol 12269. Springer, Cham (2020).

Deb, Kalyanmoy, Amrit Pratap, Sameer Agarwal and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” in IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, (2002).

Doshi-Velez, Finale, and Been Kim. “Towards a rigorous science of interpretable machine learning,” no. Ml: 1–13. http://arxiv.org/abs/1702.08608 (2017).

Emilie Kaufmann and Shivaram Kalyanakrishnan. “Information complexity in bandit subset selection”. Proceedings of Machine Learning Research (2013).

Fanaee-T, Hadi, and Joao Gama. “Event labeling combining ensemble detectors and background knowledge.” Progress in Artificial Intelligence. Springer Berlin Heidelberg, 1–15. https://doi.org/10.1007/s13748-013-0040-3. (2013).

Fernandes, Kelwin, Jaime S Cardoso, and Jessica Fernandes. “Transfer learning with partial observability applied to cervical cancer screening.” In Iberian Conference on Pattern Recognition and Image Analysis, 243–50. Springer. (2017).

Fisher, Aaron, Cynthia Rudin, and Francesca Dominici. “All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously.” http://arxiv.org/abs/1801.01489 (2018).

Fokkema, Marjolein, and Benjamin Christoffersen. “Pre: Prediction rule ensembles”. https://CRAN.R-project.org/package=pre (2017).

Friedman, Jerome H, and Bogdan E Popescu. “Predictive learning via rule ensembles.” The Annals of Applied Statistics. JSTOR, 916–54. (2008).

Friedman, Jerome H. “Greedy function approximation: A gradient boosting machine.” Annals of statistics (2001): 1189-1232.

Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. “The elements of statistical learning”. www.web.stanford.edu/~hastie/ElemStatLearn/ (2009).

Fürnkranz, Johannes, Dragan Gamberger, and Nada Lavrač. “Foundations of rule learning.” Springer Science & Business Media, (2012).

Ghorbani, Amirata, Abubakar Abid, and James Zou. “Interpretation of neural networks is fragile.” Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. 2019.

Ghorbani, Amirata, James Wexler, James Zou and Been Kim. “Towards automatic concept-based explanations.” Advances in Neural Information Processing Systems 32 (2019).

Goldstein, Alex, Adam Kapelner, Justin Bleich, and Emil Pitkin. “Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation.” journal of Computational and Graphical Statistics 24, no. 1 (2015): 44-65.

Goldstein, Alex, Adam Kapelner, Justin Bleich, and Maintainer Adam Kapelner. “Package ‘ICEbox’.” (2017).

Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. “Explaining and harnessing adversarial examples.” arXiv preprint arXiv:1412.6572 (2014).

Greenwell, Brandon M., Bradley C. Boehmke, and Andrew J. McCarthy. “A simple and effective model-based variable importance measure.” arXiv preprint arXiv:1805.04755 (2018).

Grömping, Ulrike. “Model-Agnostic Effects Plots for Interpreting Machine Learning Models.” Reports in Mathematics, Physics and Chemistry: Department II, Beuth University of Applied Sciences Berlin. Report 1/2020 (2020)

Heider, Fritz, and Marianne Simmel. “An experimental study of apparent behavior.” The American Journal of Psychology 57 (2). JSTOR: 243–59. (1944).

Holte, Robert C. “Very simple classification rules perform well on most commonly used datasets.” Machine learning 11.1 (1993): 63-90.

Hooker, Giles. “Discovering additive structure in black box functions.” Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. (2004).

Hooker, Giles. “Generalized functional anova diagnostics for high-dimensional functions of dependent variables.” Journal of Computational and Graphical Statistics 16.3 (2007): 709-732.

Inglis, Alan, Andrew Parnell, and Catherine Hurley. “Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models.” arXiv preprint arXiv:2108.04310 (2021).

Janzing, Dominik, Lenon Minorics, and Patrick Blöbaum. “Feature relevance quantification in explainable AI: A causal problem.” International Conference on Artificial Intelligence and Statistics. PMLR (2020).

Kahneman, Daniel, and Amos Tversky. “The simulation heuristic.” Stanford Univ CA Dept of Psychology. (1981).

Karimi, Amir-Hossein, Gilles Barthe, Borja Balle and Isabel Valera. “Model-agnostic counterfactual explanations for consequential decisions.” AISTATS (2020).

Karpathy, Andrej, Justin Johnson, and Li Fei-Fei. “Visualizing and understanding recurrent networks.” arXiv preprint arXiv:1506.02078 (2015).

Kaufman, Leonard, and Peter Rousseeuw. “Clustering by means of medoids”. North-Holland (1987).

Kim, Been, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, and Fernanda Viegas. “Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav).” In International conference on machine learning, pp. 2668-2677. PMLR (2018).

Kim, Been, Rajiv Khanna, and Oluwasanmi O. Koyejo. “Examples are not enough, learn to criticize! Criticism for interpretability.” Advances in Neural Information Processing Systems (2016).

Kindermans, Pieter-Jan, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, and Been Kim. “The (un) reliability of saliency methods.” In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, pp. 267-280. Springer, Cham (2019).

Koh, Pang Wei, Kai-Siang Ang, Hubert HK Teo, and Percy Liang. “On the accuracy of influence functions for measuring group effects.” arXiv preprint arXiv:1905.13289 (2019).

Koh, Pang Wei, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang. “Concept bottleneck models.” In International Conference on Machine Learning, pp. 5338-5348. PMLR (2020).

Koh, Pang Wei, and Percy Liang. “Understanding black-box predictions via influence functions.” arXiv preprint arXiv:1703.04730 (2017).

Laugel, Thibault, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. “Inverse classification for comparison-based interpretability in machine learning.” arXiv preprint arXiv:1712.08443 (2017).

Letham, Benjamin, Cynthia Rudin, Tyler H. McCormick, and David Madigan. “Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model.” The Annals of Applied Statistics 9, no. 3 (2015): 1350-1371.

Linsley, Drew, et al. “What are the visual features underlying human versus machine vision?.” Proceedings of the IEEE International Conference on Computer Vision Workshops. 2017.

Lipton, Peter. “Contrastive explanation.” Royal Institute of Philosophy Supplements 27 (1990): 247-266.

Lipton, Zachary C. “The mythos of model interpretability.” arXiv preprint arXiv:1606.03490, (2016).

Lundberg, Scott M., Gabriel G. Erion, and Su-In Lee. “Consistent individualized feature attribution for tree ensembles.” arXiv preprint arXiv:1802.03888 (2018).

Lundberg, Scott M., and Su-In Lee. “A unified approach to interpreting model predictions.” Advances in Neural Information Processing Systems (2017).

Marco Tulio Ribeiro, Sameer Singh and Carlos Guestrin. “Anchors: high-precision model-agnostic explanations”. AAAI Conference on Artificial Intelligence (AAAI), 2018

Miller, Tim. “Explanation in artificial intelligence: Insights from the social sciences.” arXiv Preprint arXiv:1706.07269. (2017).

Mothilal, Ramaravind K., Amit Sharma, and Chenhao Tan. “Explaining machine learning classifiers through diverse counterfactual explanations.” Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. (2020).

Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. “Definitions, methods, and applications in interpretable machine learning.” Proceedings of the National Academy of Sciences, 116(44), 22071-22080. (2019).

Nguyen, Anh, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune. “Synthesizing the preferred inputs for neurons in neural networks via deep generator networks.” Advances in neural information processing systems 29 (2016): 3387-3395.

Nguyen, Anh, Jeff Clune, Yoshua Bengio, Alexey Dosovitskiy, and Jason Yosinski. “Plug & play generative networks: Conditional iterative generation of images in latent space.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4467-4477. 2017.

Nickerson, Raymond S. “Confirmation Bias: A ubiquitous phenomenon in many guises.” Review of General Psychology 2 (2). Educational Publishing Foundation: 175. (1998).

Nie, Weili, Yang Zhang, and Ankit Patel. “A theoretical explanation for perplexing behaviors of backpropagation-based visualizations.” arXiv preprint arXiv:1805.07039 (2018).

Olah, Chris, Alexander Mordvintsev, and Ludwig Schubert. “Feature visualization.” Distill 2, no. 11 (2017): e7.

Olah, Chris, Arvind Satyanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine Ye, and Alexander Mordvintsev. “The building blocks of interpretability.” Distill 3, no. 3 (2018): e10.

Olga Russakovsky and Jia Deng (equal contribution), Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei. “ImageNet large scale visual recognition challenge”. IJCV (2015).

Papernot, Nicolas, et al. “Practical black-box attacks against machine learning.” Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security. ACM (2017).

Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. “Anchors: High-precision model-agnostic explanations.” AAAI Conference on Artificial Intelligence (2018).

Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. “Model-agnostic interpretability of machine learning.” ICML Workshop on Human Interpretability in Machine Learning. (2016).

Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. “Why should I trust you?: Explaining the predictions of any classifier.” Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM (2016).

Robnik-Sikonja, Marko, and Marko Bohanec. “Perturbation-based explanations of prediction models.” Human and Machine Learning. Springer, Cham. 159-175. (2018).

Selvaraju, Ramprasaath R., et al. “Grad-cam: Visual explanations from deep networks via gradient-based localization.” Proceedings of the IEEE international conference on computer vision. (2017).

Shapley, Lloyd S. “A value for n-person games.” Contributions to the Theory of Games 2.28 (1953): 307-317.

Shrikumar, Avanti, Peyton Greenside, and Anshul Kundaje. “Learning important features through propagating activation differences.” Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, (2017).

Simonyan, Karen, Andrea Vedaldi, and Andrew Zisserman. “Deep inside convolutional networks: Visualising image classification models and saliency maps.” arXiv preprint arXiv:1312.6034 (2013).

Simonyan, Karen, and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition.” arXiv preprint arXiv:1409.1556 (2014).

Slack, Dylan, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju. “Fooling lime and shap: Adversarial attacks on post hoc explanation methods.” In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180-186 (2020).

Slack, Dylan, et al. “Fooling lime and shap: Adversarial attacks on post hoc explanation methods.” Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. 2020.

Smilkov, Daniel, et al. “SmoothGrad: removing noise by adding noise.” arXiv preprint arXiv:1706.03825 (2017).

Springenberg, Jost Tobias, et al. “Striving for simplicity: The all convolutional net.” arXiv preprint arXiv:1412.6806 (2014).

Staniak, Mateusz, and Przemyslaw Biecek. “Explanations of model predictions with live and breakDown packages.” arXiv preprint arXiv:1804.01955 (2018).

Su, Jiawei, Danilo Vasconcellos Vargas, and Kouichi Sakurai. “One pixel attack for fooling deep neural networks.” IEEE Transactions on Evolutionary Computation (2019).

Sundararajan, Mukund, Ankur Taly, and Qiqi Yan. “Axiomatic attribution for deep networks.” Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017.

Sundararajan, Mukund, and Amir Najmi. “The many Shapley values for model explanation.” arXiv preprint arXiv:1908.08474 (2019).

Szegedy, Christian, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. “Rethinking the inception architecture for computer vision.” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818-2826 (2016).

Szegedy, Christian, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. “Intriguing properties of neural networks.” arXiv preprint arXiv:1312.6199 (2013).

Tomsett, Richard, Dan Harborne, Supriyo Chakraborty, Prudhvi Gurram, and Alun Preece. “Sanity checks for saliency metrics.” In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 6021-6029. 2020.

Van Looveren, Arnaud, and Janis Klaise. “Interpretable counterfactual explanations guided by prototypes.” arXiv preprint arXiv:1907.02584 (2019).

Wachter, Sandra, Brent Mittelstadt, and Chris Russell. “Counterfactual explanations without opening the black box: Automated decisions and the GDPR.” (2017).

Wei, Pengfei, Zhenzhou Lu, and Jingwen Song. “Variable importance analysis: a comprehensive review.” Reliability Engineering & System Safety 142 (2015): 399-432.

Yang, Hongyu, Cynthia Rudin, and Margo Seltzer. “Scalable Bayesian rule lists.” Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017.

Zeiler, Matthew D., and Rob Fergus. “Visualizing and understanding convolutional networks.” European conference on computer vision. Springer, Cham (2014).

Zhao, Qingyuan, and Trevor Hastie. “Causal interpretations of black-box models.” Journal of Business & Economic Statistics, to appear. (2017).

Štrumbelj, Erik, and Igor Kononenko. “A general method for visualizing and explaining black-box regression models.” In International Conference on Adaptive and Natural Computing Algorithms, 21–30. Springer. (2011).

Štrumbelj, Erik, and Igor Kononenko. “Explaining prediction models and individual predictions with feature contributions.” Knowledge and information systems 41.3 (2014): 647-665.

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