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Interpretability has become incredibly important as machine learning is increasingly used to inform consequential decisions.
Neuronlike adaptive elements that can solve difficult learning control problems
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Classification and regression trees
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Prostate specific antigen in the diagnosis and treatment of adenocarcinoma of the prostate. ii. radical prostatectomy treated patients
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Combining instance-based and model-based learning
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Born again trees
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Regression shrinkage and selection via the lasso
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Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals
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Mining high-speed data streams
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Improving predictive inference under covariate shift by weighting the log-likelihood function
Shimodaira, H · 2000
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Greedy function approximation: a gradient boosting machine
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Machine learning for medical diagnosis: history, state of the art and perspective
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Model compression
Bucilua, C., Caruana, R., and Niculescu-Mizil, A · 2006
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Seeing the forest through the trees
Van Assche, A. and Blockeel, H · 2007
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Using data mining to predict secondary school student performance
Cortez, P. and Silva, A. M. G · 2008
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Dataset shift in machine learning, 2009
Candela, J. Q., Sugiyama, M., Schwaighofer, A., and Lawrence, N. D · 2009
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Causality
Pearl, J · 2009
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Collision avoidance for unmanned aircraft using markov decision processes
Temizer, S., Kochenderfer, M., Kaelbling, L., Lozano-Pérez, T., and Kuchar, J · 2010
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Intelligible models for classification and regression
Caruana, R., Lou, Y., and Gehrke, J · 2012
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Equality of opportunity in supervised learning
Hardt, M., Price, E., Srebro, N., et al · 2016
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Interpretable decision sets: A joint framework for description and prediction
Lakkaraju, H., Bach, S. H., and Leskovec, J · 2016
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Why should i trust you?: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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Supersparse linear integer models for optimized medical scoring systems
Ustun, B. and Rudin, C · 2016
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Mediboost: a patient stratification tool for interpretable decision making in the era of precision medicine
Valdes, G., Luna, J. M., Eaton, E., Simone, C. B., et al · 2016
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Genesim: genetic extraction of a single, interpretable model
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Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R · 2012
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Interpreting tree ensembles with intrees
Deng, H · 2014
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Algorithms for interpretable machine learning
Rudin, C · 2014
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., and Elhadad, N · 2015
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Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model
Letham, B., Rudin, C., McCormick, T. H., Madigan, D., et al · 2015
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Falling rule lists
Wang, F. and Rudin, C · 2015
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Vandewiele, G., Janssens, O., Ongenae, F., De Turck, F., and Van Hoecke, S · 2016
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https://gym.openai.com/envs/Pendulum-v0
Openai pendulum-v0 environment · 2017
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Towards a rigorous science of interpretable machine learning
Doshi-Velez, F. and Kim, B · 2017
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Distilling a neural network into a soft decision tree
Frosst, N. and Hinton, G · 2017
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Simple rules for complex decisions
Jung, J., Concannon, C., Shroff, R., Goel, S., and Goldstein, D · 2017
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Human decisions and machine predictions
Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., and Mullainathan, S · 2017
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Interpretable & explorable approximations of black box models
Lakkaraju, H., Kamar, E., Caruana, R., and Leskovec, J · 2017
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Yang, H., Rudin, C., and Seltzer, M · 2017
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