Fetching the paper…
Reading the bibliography…
Automated decision support systems promise to help human experts solve multiclass classification tasks more efficiently and accurately.
The algorithmic automation problem: Prediction, triage, and human effort
Raghu, M., Blumer, K., Corrado, G., Kleinberg, J., Obermeyer, Z., and Mullainathan, S. (2019) · 1903
Earlier work this paper cites.
How model accuracy and explanation fidelity influence user trust
Papenmeier, A., Englebienne, G., and Seifert, C. (2019) · 1907
Earlier work this paper cites.
Human uncertainty makes classification more robust
Peterson, J. C., Battleday, R. M., Griffiths, T. L., and Russakovsky, O. (2019) · 1908
Earlier work this paper cites.
On the possible psychophysical laws
Luce, R. D. (1959) · 1959
Earlier work this paper cites.
Elimination by aspects: A theory of choice
Tversky, A. (1972) · 1972
Earlier work this paper cites.
Phased decision strategies: Sequels to an initial screening
Wright, P. and Barbour, F. (1977) · 1977
Earlier work this paper cites.
Adding asymmetrically dominated alternatives: Violations of regularity and the similarity hypothesis
Huber, J., Payne, J. W., and Puto, C. (1982) · 1982
Earlier work this paper cites.
Choice based on reasons: The case of attraction and compromise effects
Simonson, I. (1989) · 1989
Earlier work this paper cites.
Broadening the definition of decision making: The role of prechoice screening of options
Beach, L. R. (1993) · 1993
Earlier work this paper cites.
Context-dependent preferences
Tversky, A. and Simonson, I. (1993) · 1993
Earlier work this paper cites.
Discrete choice models with latent choice sets
Ben-Akiva, M. and Boccara, B. (1995) · 1995
Earlier work this paper cites.
Algorithmic Learning in a Random World
Vovk, V., Gammerman, A., and Shafer, G. (2005) · 2005
Earlier work this paper cites.
When humans and machines make joint decisions: A non-symmetric bandit model
Bordt, S. and von Luxburg, U. (2020) · 2007
Earlier work this paper cites.
Nourani, M., King, J. T., and Ragan, E. D. (2020) · 2008
Earlier work this paper cites.
Learning nondeterministic classifiers
Del Coz, J. J., Díez, J., and Bahamonde, A. (2009) · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al. (2009) · 2009
Earlier work this paper cites.
Conformal prediction for reliable machine learning: theory, adaptations and applications
Balasubramanian, V., Ho, S.-S., and Vovk, V. (2014) · 2014
Earlier work this paper cites.
Credal classification rule for uncertain data based on belief functions
Liu, Z.-G., Pan, Q., Dezert, J., and Mercier, G. (2014) · 2014
Earlier work this paper cites.
Discrete choice methods with simulation
Heiss, F. (2016) · 2016
Cited alongside, same era.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q. (2017) · 2017
Cited alongside, same era.
Cautious classification with nested dichotomies and imprecise probabilities
Yang, G., Destercke, S., and Masson, M.-H. (2017) · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Human decision making with machine assistance: An experiment on bailing and jailing
Grgić-Hlača, N., Engel, C., and Gummadi, K. P. (2019) · 2019
Cited alongside, same era.
Ask not what ai can do, but what ai should do: Towards a framework of task delegability
Lubars, B. and Tan, C. (2019) · 2019
Set-valued classification–overview via a unified framework
Chzhen, E., Denis, C., Hebiri, M., and Lorieul, T. (2021) · 2021
Later among the works it cites.
Classification under human assistance
De, A., Okati, N., Zarezade, A., and Gomez-Rodriguez, M. (2021) · 2021
Later among the works it cites.
Combining human predictions with model probabilities via confusion matrices and calibration
Kerrigan, G., Smyth, P., and Steyvers, M. (2021) · 2021
Later among the works it cites.
Assessing the impact of automated suggestions on decision making: Domain experts mediate model errors but take less initiative
Levy, A., Agrawal, M., Satyanarayan, A., and Sontag, D. (2021) · 2021
Later among the works it cites.
Evaluating eligibility criteria of oncology trials using real-world data and ai
Liu, R., Rizzo, S., Whipple, S., Pal, N., Pineda, A. L., Lu, M., Arnieri, B., Lu, Y., Capra, W., Copping, R., et al. (2021) · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Conformalized quantile regression
Romano, Y., Patterson, E., and Candes, E. (2019) · 2019
Cited alongside, same era.
Understanding the effect of accuracy on trust in machine learning models
Yin, M., Wortman Vaughan, J., and Wallach, H. (2019) · 2019
Cited alongside, same era.
Regression under human assistance
De, A., Koley, P., Ganguly, N., and Gomez-Rodriguez, M. (2020) · 2020
Cited alongside, same era.
Distribution-free binary classification: prediction sets, confidence intervals and calibration
Gupta, C., Podkopaev, A., and Ramdas, A. (2020) · 2020
Cited alongside, same era.
A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns
Jiao, W., Atwal, G., Polak, P., Karlic, R., Cuppen, E., Danyi, A., de Ridder, J., van Herpen, C., Lolkema, M. P., Steeghs, N., et al. (2020) · 2020
Cited alongside, same era.
Consistent estimators for learning to defer to an expert
Mozannar, H. and Sontag, D. (2020) · 2020
Cited alongside, same era.
Later among the works it cites.
Partial classification in the belief function framework
Ma, L. and Denoeux, T. (2021) · 2021
Later among the works it cites.
Efficient set-valued prediction in multi-class classification
Mortier, T., Wydmuch, M., Dembczyński, K., Hüllermeier, E., and Waegeman, W. (2021) · 2021
Later among the works it cites.
Multilabel classification with partial abstention: Bayes-optimal prediction under label independence
Nguyen, V.-L. and Hüllermeier, E. (2021) · 2021
Later among the works it cites.
Differentiable learning under triage
Okati, N., De, A., and Gomez-Rodriguez, M. (2021) · 2021
Later among the works it cites.
Distribution-free uncertainty quantification for classification under label shift
Podkopaev, A. and Ramdas, A. (2021) · 2021
Later among the works it cites.
Reinforcement learning under algorithmic triage
Straitouri, E., Singla, A., Meresht, V. B., and Gomez-Rodriguez, M. (2021) · 2021
Later among the works it cites.
Are explanations helpful? a comparative study of the effects of explanations in ai-assisted decision-making
Wang, X. and Yin, M. (2021) · 2021
Later among the works it cites.
On the utility of prediction sets in human-ai teams
Babbar, V., Bhatt, U., and Weller, A. (2022) · 2022
Closest in time.
Distribution-free finite-sample guarantees and split conformal prediction
Hulsman, R. (2022) · 2022
Closest in time.
Learning to switch among agents in a team
Meresht, V. B., De, A., Singla, A., and Gomez-Rodriguez, M. (2022) · 2022
Closest in time.
Uncalibrated models can improve human-ai collaboration
Vodrahalli, K., Gerstenberg, T., and Zou, J. (2022) · 2022
Closest in time.
Improving screening processes via calibrated subset selection
Wang, L., Joachims, T., and Gomez-Rodriguez, M. (2022) · 2022
Closest in time.