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We propose the holdout randomization test (HRT), an approach to feature selection using black box predictive models.
Mixture density networks
C. M. Bishop · 1994
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Controlling the false discovery rate: A practical and powerful approach to multiple testing
Y. Benjamini and Y. Hochberg · 1995
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The problem of regions
B. Efron and R. Tibshirani · 1998
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
J. Platt · 1999
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The control of the false discovery rate in multiple testing under dependency
Y. Benjamini and D. Yekutieli · 2001
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Statistical modeling: The two cultures
L. Breiman · 2001
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Dialogue on reverse-engineering assessment and methods
G. Stolovitzky, D. Monroe, and A. Califano · 2007
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P-values for high-dimensional regression
N. Meinshausen, L. Meier, and P. Bühlmann · 2009
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High dimensional variable selection
L. Wasserman and K. Roeder · 2009
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The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity
J. Barretina, G. Caponigro, N. Stransky, K. Venkatesan, A. A. Margolin, S. Kim, C. J. Wilson, J. Lehár, G. V. Kryukov, and D. Sonkin · 2012
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Conditional validity of inductive conformal predictors
V. Vovk · 2012
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Controlling the false discovery rate via knockoffs
R. F. Barber and E. J. Candès · 2015
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Why should I trust you?: Explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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Taking the human out of the loop: A review of Bayesian optimization
B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas · 2016
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QuickMMCTest: Quick multiple Monte Carlo testing
A. Gandy and G. Hahn · 2017
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Predicting human olfactory perception from chemical features of odor molecules
A. Keller, R. C. Gerkin, Y. Guan, A. Dhurandhar, G. Turu, B. Szalai, J. D. Mainland, Y. Ihara, C. W. Yu, R. Wolfinger, C. Vens, L. Schietgat, K. De Grave, R. Norel, , G. Stolovitzky, G. A. Cecchi, L. B. Vosshall, and P. Meyer · 2017
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A unified approach to interpreting model predictions
Bayesian approximate kernel regression with variable selection
L. Crawford, K. C. Wood, X. Zhou, and S. Mukherjee · 2018
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A. Fisher, C. Rudin, and F. Dominici · 2018
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Knockoffs for the mass: New feature importance statistics with false discovery guarantees
J. R. Gimenez, A. Ghorbani, and J. Zou · 2018
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Distribution-free predictive inference for regression
J. Lei, M. G’Sell, A. Rinaldo, R. J. Tibshirani, and L. Wasserman · 2018
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Bayesian neural networks for selection of drug sensitive genes
F. Liang, Q. Li, and L. Zhou · 2018
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S. M. Lundberg and S.-I. Lee · 2017
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Gene hunting with knockoffs for hidden markov models
M. Sesia, C. Sabatti, and E. J. Candès · 2017
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Learning important features through propagating activation differences
A. Shrikumar, P. Greenside, and A. Kundaje · 2017
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Iterative random forests to discover predictive and stable high-order interactions
S. Basu, K. Kumbier, J. B. Brown, and B. Yu · 2018
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The conditional permutation test
T. B. Berrett, Y. Wang, R. F. Barber, and R. J. Samworth · 2018
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Panning for gold: ‘Model-X’ knockoffs for high dimensional controlled variable selection
E. Candes, Y. Fan, L. Janson, and J. Lv · 2018
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Learning to explain: An information-theoretic perspective on model interpretation
J. Chen, L. Song, M. J. Wainwright, and M. I. Jordan · 2018
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Y. Y. Lu, J. Lv, Y. Fan, and W. S. Noble · 2018
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Mimic and classify: A meta-algorithm for conditional independence testing
R. Sen, K. Shanmugam, H. Asnani, A. Rahimzamani, and S. Kannan · 2018
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The hardness of conditional independence testing and the generalised covariance measure
R. D. Shah and J. Peters · 2018
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Black box FDR
W. Tansey, Y. Wang, D. M. Blei, and R. Rabadan · 2018
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KnockoffGAN: Generating knockoffs for feature selection using generative adversarial networks
J. Jordon, J. Yoon, and M. van der Schaar · 2019
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Deep knockoffs
Y. Romano, M. Sesia, and E. J. Candès · 2019
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