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This paper examines the stability of learned explanations for black-box predictions via model distillation with decision trees.
A multiple comparison procedure for comparing several treatments with a control
Dunnett, C. W. (1955) · 1955
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Classification and regression trees
Breiman, L., J. Friedman, C. J. Stone, and R. A. Olshen (1984) · 1984
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Generating production rules from decision trees
Quinlan, J. R. (1987) · 1987
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Controlling the false discovery rate: a practical and powerful approach to multiple testing
Benjamini, Y. and Y. Hochberg (1995) · 1995
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Extracting tree-structured representations of trained networks
Craven, M. W. and J. W. Shavlik (1995) · 1995
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Breast cancer diagnosis and prognosis via linear programming
Mangasarian, O. L., W. N. Street, and W. H. Wolberg (1995) · 1995
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Random forests
Breiman, L. (2001) · 2001
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Greedy function approximation: a gradient boosting machine
Friedman, J. H. (2001) · 2001
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Confidence sets for split points in decision trees
Banerjee, M., I. W. McKeague, et al. (2007) · 2007
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Generalized functional anova diagnostics for high-dimensional functions of dependent variables
Hooker, G. (2007) · 2007
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Evolving decision trees using oracle guides
Johansson, U. and L. Niklasson (2009) · 2009
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Standard errors for bagged and random forest estimators
Sexton, J. and P. Laake (2009) · 2009
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Oracle coached decision trees and lists
Johansson, U., C. Sönströd, and T. Löfström (2010) · 2010
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One tree to explain them all
Johansson, U., C. Sönströd, and T. Löfström (2011) · 2011
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Reverse engineering the neural networks for rule extraction in classification problems
Augasta, M. G. and T. Kathirvalavakumar (2012) · 2012
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Imitation learning by coaching
He, H., J. Eisner, and H. Daume (2012) · 2012
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Intelligible models for classification and regression
Lou, Y., R. Caruana, and J. Gehrke (2012) · 2012
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The computerized adaptive diagnostic test for major depressive disorder (cad-mdd): a screening tool for depression
Gibbons, R. D., G. Hooker, M. D. Finkelman, D. J. Weiss, P. A. Pilkonis, E. Frank, T. Moore, and D. J. Kupfer (2013) · 2013
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Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation
Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks
Angwin, J., J. Larson, S. Mattu, and L. Kirchner (2016) · 2016
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Athey, S., J. Tibshirani, and S. Wager (2016) · 2016
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Inherent trade-offs in the fair determination of risk scores
Kleinberg, J., S. Mullainathan, and M. Raghavan (2016) · 2016
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How we analyzed the compas recidivism algorithm
Larson, J., S. Mattu, L. Kirchner, and J. Angwin (2016) · 2016
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Quantifying uncertainty in random forests via confidence intervals and hypothesis tests
Mentch, L. and G. Hooker (2016) · 2016
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Goldstein, A., A. Kapelner, J. Bleich, and E. Pitkin (2013, 09) · 2013
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UCI machine learning repository
Lichman, M. (2013) · 2013
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Failure analysis of parameter-induced simulation crashes in climate models
Lucas, D., R. Klein, J. Tannahill, D. Ivanova, S. Brandon, D. Domyancic, and Y. Zhang (2013) · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., A. Vedaldi, and A. Zisserman (2013) · 2013
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Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution
Zhou, Y. and G. Hooker (2018) · 2013
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C4. 5: programs for machine learning
Quinlan, J. R. (2014) · 2014
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Confidence intervals for random forests: The jackknife and the infinitesimal jackknife
Wager, S., T. Hastie, and B. Efron (2014) · 2014
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Why should i trust you?: Explaining the predictions of any classifier
Ribeiro, M. T., S. Singh, and C. Guestrin (2016) · 2016
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · 2017
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Formal hypothesis tests for additive structure in random forests
Mentch, L. and G. Hooker (2017) · 2017
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Detecting bias in black-box models using transparent model distillation
Tan, S., R. Caruana, G. Hooker, and Y. Lou (2017) · 2017
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Estimation and inference of heterogeneous treatment effects using random forests
Wager, S. and S. Athey (2017) · 2017
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Boosting random forests to reduce bias; one-step boosted forest and its variance estimate
Ghosal, I. and G. Hooker (2018) · 2018
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Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP)
Hu, L., J. Chen, V. N. Nair, and A. Sudjianto (2018, June) · 2018
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