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Decision support systems for classification tasks are predominantly designed to predict the value of the ground truth labels.
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Imagenet large scale visual recognition challenge
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Very deep convolutional networks for large-scale image recognition
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Causal bandits: Learning good interventions via causal inference
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Mooc dropout prediction: How to measure accuracy?
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Cautious classification with nested dichotomies and imprecise probabilities
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The accuracy, fairness, and limits of predicting recidivism
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Conformal prediction under covariate shift
Tibshirani, R. J., Barber, R. F., Candes, E. J., and Ramdas, A. (2020) · 2020
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Effect of confidence and explanation on accuracy and trust calibration in ai-assisted decision making
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Distribution-free, risk-controlling prediction sets
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Set-valued classification–overview via a unified framework
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Classification under human assistance
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Adaptive conformal inference under distribution shift
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Contextual bandits for adapting treatment in a mouse model of de novo carcinogenesis
Durand, A., Achilleos, C., Iacovides, D., Strati, K., Mitsis, G. D., and Pineau, J. (2018) · 2018
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Structural causal bandits: where to intervene?
Lee, S. and Bareinboim, E. (2018) · 2018
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Mnl-bandit: A dynamic learning approach to assortment selection
Agrawal, S., Avadhanula, V., Goyal, V., and Zeevi, A. (2019) · 2019
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Beyond accuracy: The role of mental models in human-ai team performance
Bansal, G., Nushi, B., Kamar, E., Lasecki, W. S., Weld, D. S., and Horvitz, E. (2019) · 2019
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Interactive anomaly detection on attributed networks
Ding, K., Li, J., and Liu, H. (2019) · 2019
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Ask not what ai can do, but what ai should do: Towards a framework of task delegability
Lubars, B. and Tan, C. (2019) · 2019
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Gibbs, I. and Candes, E. (2021) · 2021
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Towards a science of human-ai decision making: a survey of empirical studies
Lai, V., Chen, C., Liao, Q. V., Smith-Renner, A., and Tan, C. (2021) · 2021
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Partial classification in the belief function framework
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Efficient set-valued prediction in multi-class classification
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Multilabel classification with partial abstention: Bayes-optimal prediction under label independence
Nguyen, V.-L. and Hüllermeier, E. (2021) · 2021
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Differentiable learning under triage
Okati, N., De, A., and Gomez-Rodriguez, M. (2021) · 2021
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Distribution-free uncertainty quantification for classification under label shift
Podkopaev, A. and Ramdas, A. (2021) · 2021
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Are explanations helpful? a comparative study of the effects of explanations in ai-assisted decision-making
Wang, X. and Yin, M. (2021) · 2021
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On the utility of prediction sets in human-ai teams
Babbar, V., Bhatt, U., and Weller, A. (2022) · 2022
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Conformal prediction is robust to label noise
Einbinder, B.-S., Bates, S., Angelopoulos, A. N., Gendler, A., and Romano, Y. (2022) · 2022
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Causal bandits without prior knowledge using separating sets
Kroon, A. D., Mooij, J., and Belgrave, D. (2022) · 2022
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Bayesian modeling of human–ai complementarity
Steyvers, M., Tejeda, H., Kerrigan, G., and Smyth, P. (2022) · 2022
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Uncalibrated models can improve human-ai collaboration
Vodrahalli, K., Gerstenberg, T., and Zou, J. (2022) · 2022
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Conformal prediction: A gentle introduction
Angelopoulos, A. N. and Bates, S. (2023) · 2023
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Learning human-compatible representations for case-based decision support
Liu, H., Tian, Y., Chen, C., Feng, S., Chen, Y., and Tan, C. (2023) · 2023
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Improving expert predictions with conformal prediction
Straitouri, E., Wang, L., Okati, N., and Gomez-Rodriguez, M. (2023) · 2023
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Conformal prediction under ambiguous ground truth
Stutz, D., Roy, A. G., Matejovicova, T., Strachan, P., Cemgil, A. T., and Doucet, A. (2023) · 2023
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