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We study the problem of classification with a reject option for a fixed predictor, applicable in natural language processing.
An optimum character recognition system using decision functions
C. K. Chow · 1957
Earlier work this paper cites.
On optimum recognition error and reject tradeoff
C. K. Chow · 1970
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An optimal reject rule for binary classifiers
F. Tortorella · 2000
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On optimal reject rules and roc curves, 2005
C. M. Santos-Pereira and A. M. Pires · 2005
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The interaction between classification and reject performance for distance-based reject-option classifiers
T. C. Landgrebe, D. M. Tax, P. Paclík, and R. P. Duin · 2006
Earlier work this paper cites.
On the use of roc analysis for the optimization of abstaining classifiers
T. Pietraszek · 2007
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How to compare different loss functions and their risks
I. Steinwart · 2007
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Classification with a reject option using a hinge loss
P. L. Bartlett and M. H. Wegkamp · 2008
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Support vector machines with a reject option
Y. Grandvalet, A. Rakotomamonjy, J. Keshet, and S. Canu · 2008
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Classification methods with reject option based on convex risk minimization
M. Yuan and M. Wegkamp · 2010
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Learning with rejection
C. Cortes, G. DeSalvo, and M. Mohri · 2016
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Boosting with abstention
C. Cortes, G. DeSalvo, and M. Mohri · 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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A baseline for detecting misclassified and out-of-distribution examples in neural networks, 2016
D. Hendrycks and K. Gimpel · 2016
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Selective classification for deep neural networks, 2017
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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Confidence modeling for neural semantic parsing, 2018
L. Dong, C. Quirk, and M. Lapata · 2018
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Understanding measures of uncertainty for adversarial example detection
L. Smith and Y. Gal · 2018
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The relationship between agnostic selective classification, active learning and the disagreement coefficient
Selective question answering under domain shift
A. Kamath, R. Jia, and P. Liang · 2020
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On faithfulness and factuality in abstractive summarization
J. Maynez, S. Narayan, B. Bohnet, and R. McDonald · 2020
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mt5: A massively multilingual pre-trained text-to-text transformer, 2020
L. Xue, N. Constant, A. Roberts, M. Kale, R. Al-Rfou, A. Siddhant, A. Barua, and C. Raffel · 2020
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Classification with rejection based on cost-sensitive classification
N. Charoenphakdee, Z. Cui, Y. Zhang, and M. Sugiyama · 2021
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Decontextualization: Making sentences stand-alone
E. Choi, J. Palomaki, M. Lamm, T. Kwiatkowski, D. Das, and M. Collins · 2021
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R. Gelbhart and R. El-Yaniv · 2019
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Calibration of encoder decoder models for neural machine translation, 2019
A. Kumar and S. Sarawagi · 2019
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension, 2019
M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer · 2019
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On the calibration of multiclass classification with rejection
C. Ni, N. Charoenphakdee, J. Honda, and M. Sugiyama · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer, 2019
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu · 2019
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Controlled hallucinations:learning to generate faithfully from noisy data
K. Filippova · 2020
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How can we know when language models know? on the calibration of language models for question answering, 2020
Z. Jiang, J. Araki, H. Ding, and G. Neubig · 2020
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Will this question be answered? question filtering via answer model distillation for efficient question answering
S. Garg and A. Moschitti · 2021
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Machine learning with a reject option: A survey, 2021
K. Hendrickx, L. Perini, D. Van der Plas, W. Meert, and J. Davis · 2021
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The art of abstention: Selective prediction and error regularization for natural language processing
J. Xin, R. Tang, Y. Yu, and J. Lin · 2021
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Knowing more about questions can help: Improving calibration in question answering, 2021
S. Zhang, C. Gong, and E. Choi · 2021
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H-consistency bounds for surrogate loss minimizers
P. Awasthi, A. Mao, M. Mohri, and Y. Zhong · 2022
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Scaling up models and data with t5x
A. Roberts, H. W. Chung, A. Levskaya, G. Mishra, J. Bradbury, D. Andor, S. Narang, B. Lester, C. Gaffney, A. Mohiuddin, C. Hawthorne, A. Lewkowycz, A. Salcianu, M. van Zee, J. Austin, S. Goodman, L. B. Soares, H. Hu, S. Tsvyashchenko, A. Chowdhery, J. Bastings, J. Bulian, X. Garcia, J. Ni, A. Chen, K. Kenealy, J. H. Clark, S. Lee, D. Garrette, J. Lee-Thorp, C. Raffel, N. Shazeer, M. Ritter, M. Bosma, A. Passos, J. Maitin-Shepard, N. Fiedel, M. Omernick, B. Saeta, R. Sepassi, A. Spiridonov, J. Newlan, and A. Gesmundo · 2022
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Towards improving selective prediction ability of nlp systems
N. Varshney, S. Mishra, and C. Baral · 2022
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