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Machine learning models have had discernible achievements in a myriad of applications.
Extracting refined rules from knowledge-based neural networks
G. G. Towell and J. W. Shavlik · 1993
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Case-based reasoning: Foundational issues, methodological variations, and system approaches
A. Aamodt and E. Plaza · 1994
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Extracting tree-structured representations of trained networks
M. Craven and J. W. Shavlik · 1996
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Independent component analysis: algorithms and applications
A. Hyvärinen and E. Oja · 2000
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Greedy function approximation: a gradient boosting machine
J. H. Friedman · 2001
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Extracting decision trees from trained neural networks
O. Boz · 2002
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Causality: models, reasoning, and inference, by judea pearl, cambridge university press, 2000
L. Gerson Neuberg · 2003
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Causal interpretations of black-box models
Q. Zhao and T. Hastie · 2003
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A tutorial on spectral clustering
U. Von Luxburg · 2007
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20 newsgroups
K. Lang · 2008
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Visualizing higher-layer features of a deep network
D. Erhan, Y. Bengio, A. Courville, and P. Vincent · 2009
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Causality
J. Pearl · 2009
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The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
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Principal component analysis
I. Jolliffe · 2011
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Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon
D. Boyd and K. Crawford · 2012
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Intelligible models for classification and regression
Y. Lou, R. Caruana, and J. Gehrke · 2012
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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Building high-level features using large scale unsupervised learning
Q. V. Le · 2013
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Accurate intelligible models with pairwise interactions
Y. Lou, R. Caruana, J. Gehrke, and G. Hooker · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2014
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Toward an instructionally oriented theory of example-based learning
A. Renkl · 2014
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Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
R. Caruana, Y. Lou, J. Gehrke, P. Koch, M. Sturm, and N. Elhadad · 2015
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Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation
A. Goldstein, A. Kapelner, J. Bleich, and E. Pitkin · 2015
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
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Deepfool: a simple and accurate method to fool deep neural networks
S. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2015
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Inceptionism: Going deeper into neural networks, 2015
A. Mordvintsev, C. Olah, and M. Tyka · 2015
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The limitations of deep learning in adversarial settings
N. Papernot, P. D. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami · 2015
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Show, attend and tell: Neural image caption generation with visual attention
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
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False positives, false negatives, and false analyses: A rejoinder to “machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks.”
A. Flores, K. Bechtel, and C. Lowenkamp · 2016
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Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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Eu regulations on algorithmic decision-making and a ”right to explanation”, 2016
B. Goodman and S. Flaxman · 2016
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Examples are not enough, learn to criticize! criticism for interpretability
B. Kim, R. Khanna, and O. O. Koyejo · 2016
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Examples are not enough, learn to criticize! criticism for interpretability
B. Kim, O. Koyejo, and R. Khanna · 2016
Cited alongside, same era.
The mythos of model interpretability
Z. C. Lipton · 2016
Cited alongside, same era.
Hierarchical question-image co-attention for visual question answering
J. Lu, J. Yang, D. Batra, and D. Parikh · 2016
Cited alongside, same era.
Model-agnostic interpretability of machine learning
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
Cited alongside, same era.
Why should i trust you?: Explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
Cited alongside, same era.
Multimodal explanations by predicting counterfactuality in videos
A. Kanehira, K. Takemoto, S. Inayoshi, and T. Harada · 2018
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Feature selection: A data perspective
J. Li, K. Cheng, S. Wang, F. Morstatter, R. P. Trevino, J. Tang, and H. Liu · 2018
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Fairness through causal awareness: Learning latent-variable models for biased data
D. Madras, E. Creager, T. Pitassi, and R. S. Zemel · 2018
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Explaining deep learning models using causal inference
T. Narendra, A. Sankaran, D. Vijaykeerthy, and S. Mani · 2018
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The building blocks of interpretability
C. Olah, A. Satyanarayan, I. Johnson, S. Carter, L. Schubert, K. Ye, and A. Mordvintsev · 2018
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H. Xu and K. Saenko · 2016
Cited alongside, same era.
Stacked attention networks for image question answering
Z. Yang, X. He, J. Gao, L. Deng, and A. Smola · 2016
Cited alongside, same era.
Hierarchical attention networks for document classification
Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy · 2016
Cited alongside, same era.
A causal framework for explaining the predictions of black-box sequence-to-sequence models
D. Alvarez-Melis and T. Jaakkola · 2017
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
A. Chouldechova · 2017
Cited alongside, same era.
Towards a rigorous science of interpretable machine learning
F. Doshi-Velez and B. Kim · 2017
Cited alongside, same era.
UCI machine learning repository, 2017
D. Dua and C. Graff · 2017
Cited alongside, same era.
Theoretical impediments to machine learning with seven sparks from the causal revolution
J. Pearl · 2018
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Model agnostic supervised local explanations
G. Plumb, D. Molitor, and A. S. Talwalkar · 2018
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Introduction to data mining
P.-N. Tan · 2018
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Fairness in decision-making – the causal explanation formula
J. Zhang and E. Bareinboim · 2018
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Visual interpretability for deep learning: a survey
Q.-s. Zhang and S.-C. Zhu · 2018
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Machine bias
J. Angwin, J. Larson, L. Kirchner, and S. Mattu · 2019
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Neural network attributions: A causal perspective
A. Chattopadhyay, P. Manupriya, A. Sarkar, and V. N. Balasubramanian · 2019
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Incorporating interpretability into latent factor models via fast influence analysis
W. Cheng, Y. Shen, L. Huang, and Y. Zhu · 2019
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Interpretation of neural networks is fragile
A. Ghorbani, A. Abid, and J. Zou · 2019
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Explaining classifiers with causal concept effect (cace)
Y. Goyal, U. Shalit, and B. Kim · 2019
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Counterfactual visual explanations
Y. Goyal, Z. Wu, J. Ernst, D. Batra, D. Parikh, and S. Lee · 2019
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Learning interpretable models with causal guarantees
C. Kim and O. Bastani · 2019
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On the accuracy of influence functions for measuring group effects
P. W. Koh, K.-S. Ang, H. H. Teo, and P. Liang · 2019
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Causal learning in question quality improvement
Y. Li, R. Guo, W. Wang, and H. Liu · 2019
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Generative counterfactual introspection for explainable deep learning
S. Liu, B. Kailkhura, D. Loveland, and Y. Han · 2019
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Interpretable counterfactual explanations guided by prototypes
A. V. Looveren and J. Klaise · 2019
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Explainable reinforcement learning through a causal lens
P. Madumal, T. Miller, L. Sonenberg, and F. Vetere · 2019
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A survey on bias and fairness in machine learning
N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan · 2019
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Interpretable Machine Learning
C. Molnar · 2019
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Interpretable machine learning
C. Molnar · 2019
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Explaining deep learning models with constrained adversarial examples
J. Moore, N. Hammerla, and C. Watkins · 2019
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Explaining machine learning classifiers through diverse counterfactual explanations
R. K. Mothilal, A. Sharma, and C. Tan · 2019
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Explaining visual models by causal attribution
Á. Parafita and J. Vitrià · 2019
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The seven tools of causal inference, with reflections on machine learning
J. Pearl · 2019
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Generating counterfactual and contrastive explanations using SHAP
S. Rathi · 2019
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Evaluating explanation without ground truth in interpretable machine learning, 2019
F. Yang, M. Du, and X. Hu · 2019
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Interpreting cnns via decision trees
Q. Zhang, Y. Yang, H. Ma, and Y. N. Wu · 2019
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Amazon customer reviews dataset
AWS · 2020
Closest in time.
Imdb datasets
IMDb · 2020
Closest in time.
The mnist database
Y. LeCun, C. Cortes, and C. Burges · 2020
Closest in time.
Uci machine learning repository
UCI · 2020
Closest in time.
Yelp dataset
YELP · 2020
Closest in time.