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Training reliable deep learning models which avoid making overconfident but incorrect predictions is a longstanding challenge.
Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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On the foundations of noise-free selective classification
R. El-Yaniv and Y. Wiener · 2010
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Probabilistic inference and differential privacy
O. Williams and F. McSherry · 2010
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Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
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Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark
S. Houben, J. Stallkamp, J. Salmen, M. Schlipsing, and C. Igel · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
R. Bassily, A. Smith, and A. Thakurta · 2014
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The algorithmic foundations of differential privacy
C. Dwork, A. Roth, et al · 2014
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Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
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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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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 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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Selective classification for deep neural networks
Y. Geifman and R. El-Yaniv · 2017
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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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Finite sample differentially private confidence intervals
V. Karwa and S. Vadhan · 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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Rényi differential privacy
I. Mironov · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
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Bootstrap inference and differential privacy: Standard errors for free
Selective classification can magnify disparities across groups
E. Jones, S. Sagawa, P. W. Koh, A. Kumar, and P. Liang · 2020
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Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
J. Liu, Z. Lin, S. Padhy, D. Tran, T. Bedrax Weiss, and B. Lakshminarayanan · 2020
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Consistent estimators for learning to defer to an expert
H. Mozannar and D. Sontag · 2020
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Stock closing price prediction using machine learning techniques
M. Vijh, D. Chandola, V. A. Tikkiwal, and A. Kumar · 2020
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Regression with reject option and application to knn
A. Zaoui, C. Denis, and M. Hebiri · 2020
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Unbiased statistical estimation and valid confidence intervals under differential privacy
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T. Brawner and J. Honaker · 2018
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Differential privacy has disparate impact on model accuracy
E. Bagdasaryan, O. Poursaeed, and V. Shmatikov · 2019
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Artificial intelligence, bias and clinical safety
R. Challen, J. Denny, M. Pitt, L. Gompels, T. Edwards, and K. Tsaneva-Atanasova · 2019
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Statistically valid inferences from privacy protected data
G. Evans, G. King, M. Schwenzfeier, and A. Thakurta · 2019
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Selectivenet: A deep neural network with an integrated reject option
Y. Geifman and R. El-Yaniv · 2019
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Deep gamblers: Learning to abstain with portfolio theory
Z. Liu, Z. Wang, P. P. Liang, R. R. Salakhutdinov, L.-P. Morency, and M. Ueda · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
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C. Covington, X. He, J. Honaker, and G. Kamath · 2021
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Generating and characterizing scenarios for safety testing of autonomous vehicles
Z. Ghodsi, S. K. S. Hari, I. Frosio, T. Tsai, A. Troccoli, S. W. Keckler, S. Garg, and A. Anandkumar · 2021
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Understanding the societal impacts of machine translation: a critical review of the literature on medical and legal use cases
L. N. Vieira, M. O’Hagan, and C. O’Sullivan · 2021
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Opacus: User-friendly differential privacy library in PyTorch
A. Yousefpour, I. Shilov, A. Sablayrolles, D. Testuggine, K. Prasad, M. Malek, J. Nguyen, S. Ghosh, A. Bharadwaj, J. Zhao, G. Cormode, and I. Mironov · 2021
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Parametric bootstrap for differentially private confidence intervals
C. Ferrando, S. Wang, and D. Sheldon · 2022
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Selective classification via neural network training dynamics
S. Rabanser, A. Thudi, K. Hamidieh, A. Dziedzic, and N. Papernot · 2022
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Recycling scraps: Improving private learning by leveraging intermediate checkpoints
V. Shejwalkar, A. Ganesh, R. Mathews, O. Thakkar, and A. Thakurta · 2022
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Towards better selective classification
L. Feng, M. O. Ahmed, H. Hajimirsadeghi, and A. H. Abdi · 2023
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