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The learning to defer (L2D) framework has the potential to make AI systems safer.
An optimum character recognition system using decision functions
Chow, C. K · 1957
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Solving multiclass learning problems via error-correcting output codes
Dietterich, T. G. and Bakiri, G · 1995
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Reducing multiclass to binary: A unifying approach for margin classifiers
Allwein, E. L., Schapire, R. E., and Singer, Y · 2001
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In defense of one-vs-all classification
Rifkin, R. and Klautau, A · 2004
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Loss functions for binary class probability estimation and classification: Structure and applications
Buja, A., Stuetzle, W., and Shen, Y · 2005
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Sensitive error correcting output codes
Langford, J., Tti-Chicago, Net, J., and Beygelzimer, A · 2005
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Convexity, classification, and risk bounds
Bartlett, P. L., Jordan, M. I., and McAuliffe, J. D · 2006
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How to compare different loss functions and their risks
Steinwart, I · 2007
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Galaxy Zoo: the dependence of morphology and colour on environment*
Bamford, S. P., Nichol, R. C., Baldry, I. K., Land, K., Lintott, C. J., Schawinski, K., Slosar, A., Szalay, A. S., Thomas, D., Torki, M., Andreescu, D., Edmondson, E. M., Miller, C. J., Murray, P., Raddick, M. J., and Vandenberg, J · 2008
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Classification with a reject option using a hinge loss
Bartlett, P. L. and Wegkamp, M. H · 2008
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Support vector machines with a reject option
Grandvalet, Y., Rakotomamonjy, A., Keshet, J., and Canu, S · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Composite binary losses
Reid, M. D. and Williamson, R. C · 2010
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Classification methods with reject option based on convex risk minimization
Yuan, M. and Wegkamp, M · 2010
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Convolutional neural networks for sentence classification
Kim, Y · 2014
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On the consistency of output code based learning algorithms for multiclass learning problems
Ramaswamy, H. G., Srinivasan Babu, B., Agarwal, S., and Williamson, R. C · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., and Clune, J · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Fasttext.zip: Compressing text classification models
Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., and Mikolov, T · 2016
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Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
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Automated hate speech detection and the problem of offensive language
Davidson, T., Warmsley, D., Macy, M. W., and Weber, I · 2017
On the calibration of multiclass classification with rejection
Ni, C., Charoenphakdee, N., Honda, J., and Sugiyama, M · 2019
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The algorithmic automation problem: Prediction, triage, and human effort
Raghu, M., Blumer, K., Corrado, G., Kleinberg, J., Obermeyer, Z., and Mullainathan, S · 2019
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Evaluating model calibration in classification
Vaicenavicius, J., Widmann, D., Andersson, C., Lindsten, F., Roll, J., and Schön, T · 2019
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A survey of deep learning techniques for autonomous driving
Grigorescu, S., Trasnea, B., Cocias, T., and Macesanu, G · 2020
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Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images
Kadampur, M. A. and Al Riyaee, S · 2020
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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To trust or not to trust a classifier
Jiang, H., Kim, B., Guan, M. Y., and Gupta, M · 2018
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Predict responsibly: Improving fairness and accuracy by learning to defer
Madras, D., Pitassi, T., and Zemel, R · 2018
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Consistent algorithms for multiclass classification with an abstain option
Ramaswamy, H. G., Tewari, A., and Agarwal, S · 2018
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The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Tschandl, P., Rosendahl, C., and Kittler, H · 2018
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Legal judgment prediction via topological learning
Zhong, H., Guo, Z., Tu, C., Xiao, C., Liu, Z., and Sun, M · 2018
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Consistent estimators for learning to defer to an expert
Mozannar, H. and Sontag, D. A · 2020
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Human–computer collaboration for skin cancer recognition
Tschandl, P., Rinner, C., Apalla, Z., Argenziano, G., Codella, N. C. F., Halpern, A. C., Janda, M., Lallas, A., Longo, C., Malvehy, J., Paoli, J., Puig, S., Rosendahl, C., Soyer, H. P., Zalaudek, I., and Kittler, H · 2020
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Learning to complement humans
Wilder, B., Horvitz, E., and Kamar, E · 2020
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Is the most accurate ai the best teammate? optimizing ai for teamwork
Bansal, G., Nushi, B., Kamar, E., Horvitz, E., and Weld, D. S · 2021
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Classification with rejection based on cost-sensitive classification
Charoenphakdee, N., Cui, Z., Zhang, Y., and Sugiyama, M · 2021
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Differentiable learning under triage
Okati, N., De, A., and Gomez-Rodriguez, M · 2021
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Mlp-mixer: An all-mlp architecture for vision
Tolstikhin, I. O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Keysers, D., Uszkoreit, J., Lucic, M., and Dosovitskiy, A · 2021
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Calibrating predictions to decisions: A novel approach to multi-class calibration
Zhao, S., Kim, M., Sahoo, R., Ma, T., and Ermon, S · 2021
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Machine learning-based prediction of covid-19 diagnosis based on symptoms
Zoabi, Y., Deri-Rozov, S., and Shomron, N · 2021
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Top-label calibration and multiclass-to-binary reductions
Gupta, C. and Ramdas, A · 2022
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