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To use machine learning in high stakes applications (e.g.
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Classification with reject option
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Support vector machines with a reject option
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P.J. Huber and E.M. Ronchetti · 2009
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Dataset Shift in Machine Learning
J. Quiñonero Candela, M. Sugiyama, A. Schwaighofer, and N.D. Lawrence · 2009
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Practical variational inference for neural networks
A. Graves · 2011
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Bayesian learning via stochastic gradient Langevin dynamics
M. Welling and Y.W. Teh · 2011
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Bayesian posterior sampling via stochastic gradient Fisher scoring
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Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Y. Gal and Z. Ghahramani · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
D. Hendrycks and K. Gimpel · 2016
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Black-box α \alpha -divergence minimization
J.M. Hernández-Lobato, Y. Li, M. Rowland, D. Hernández-Lobato, T. Bui, and R.E. Turner · 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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Why should I trust you?: Explaining the predictions of any classifier
M.T. Ribeiro, S. Singh, and C. Guestrin · 2016
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Practical Gauss-Newton optimisation for deep learning
A. Botev, H. Ritter, and D. Barber · 2017
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Y. Bengio, A. Courville, and P. Vincent · 2013
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Learning hierarchical features for scene labeling
C. Farabet, C. Couprie, L. Najman, and Y. LeCun · 2013
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Build, compute, critique, repeat: Data analysis with latent variable models
D.M. Blei · 2014
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Weight uncertainty in neural networks
C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra · 2015
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Probabilistic backpropagation for scalable learning of Bayesian neural networks
J.M. Hernández-Lobato and R. Adams · 2015
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On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K.Q. Weinberger · 2017
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Understanding black-box predictions via influence functions
P.W. Koh and P. Liang · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
B. Lakshminarayanan, A. Pritzel, and C. Blundell · 2017
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Reliable decision support using counterfactual models
P. Schulam and S. Saria · 2017
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Does mitigating ML’s impact disparity require treatment disparity?
Z.C. Lipton, A. Chouldechova, and J. McAuley · 2018
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Open category detection with pac guarantees
S. Liu, R. Garrepalli, T.G. Dietterich, A. Fern, and D. Hendrycks · 2018
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Towards accountable AI: Hybrid human-machine analyses for characterizing system failure
B. Nushi, E. Kamar, and E. Horvitz · 2018
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A scalable Laplace approximation for neural networks
H. Ritter, A. Botev, and D. Barber · 2018
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Scalable joint models for reliable uncertainty-aware event prediction
H. Soleimani, J. Hensman, and S. Saria · 2018
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Counterfactual normalization: proactively addressing dataset shift using causal mechanisms
A. Subbaswamy and S. Saria · 2018
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Tutorial: Safe and reliable machine learning
S. Saria and A. Subbaswamy · 2019
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Preventing failures due to dataset shift: Learning predictive models that transport
A. Subbaswamy, P. Schulam, and S. Saria · 2019
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