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Machine learning has shown much promise in helping improve the quality of medical, legal, and financial decision-making.
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Robert Tibshirani · 1996
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Leo Breiman · 2001
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Greedy function approximation: a gradient boosting machine
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Rademacher and gaussian complexities: Risk bounds and structural results
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Bayesian nonparametric modeling for causal inference
Jennifer L Hill · 2011
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The battle trial: personalizing therapy for lung cancer
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Machine Learning: A Probabilistic Perspective
Kevin P Murphy · 2012
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Online decision-making with high-dimensional covariates
Hamsa Bastani and Mohsen Bayati · 2015
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad · 2015
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Unsupervised learning by program synthesis
Kevin Ellis, Armando Solar-Lezama, and Josh Tenenbaum · 2015
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A targeted real-time early warning score (trewscore) for septic shock
Katharine E Henry, David N Hager, Peter J Pronovost, and Suchi Saria · 2015
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Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Susan Athey and Guido Imbens · 2016
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Equality of opportunity in supervised learning
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Towards a rigorous science of interpretable machine learning
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Distilling a neural network into a soft decision tree
Nicholas Frosst and Geoffrey Hinton · 2017
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Human decisions and machine predictions
Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan · 2017
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Interpretable & explorable approximations of black box models
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, and Jure Leskovec · 2017
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Himabindu Lakkaraju, Stephen H Bach, and Jure Leskovec · 2016
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Statistical Learning Theory
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Estimating individual treatment effect: generalization bounds and algorithms
Uri Shalit, Fredrik D Johansson, and David Sontag · 2017
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Estimation and inference of heterogeneous treatment effects using random forests
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Hongyu Yang, Cynthia Rudin, and Margo Seltzer · 2017
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Verifiable reinforcement learning via policy extraction
Osbert Bastani, Yewen Pu, and Armando Solar-Lezama · 2018
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Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, and Josh Tenenbaum · 2018
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Human-in-the-loop interpretability prior
Isaac Lage, Andrew Ross, Samuel J Gershman, Been Kim, and Finale Doshi-Velez · 2018
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Houdini: Lifelong learning as program synthesis
Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, and Swarat Chaudhuri · 2018
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Programmatically interpretable reinforcement learning
Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, and Swarat Chaudhuri · 2018
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Adapting neural networks for the estimation of treatment effects
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