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An agnostic PAC learning algorithm finds a predictor that is competitive with the best predictor in a benchmark hypothesis class, where competitiveness is measured with respect to a given loss function.
Advancing subgroup fairness via sleeping experts
Blum, A. and Lykouris, T. (2019) · 1909
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Recovering from biased data: Can fairness constraints improve accuracy?
Blum, A. and Stangl, K. (2019) · 1912
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Multicalibration: Calibration for the (computationally-identifiable) masses
Hébert-Johnson, Ú., Kim, M. P., Reingold, O., and Rothblum, G. (2018) · 1948
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The well-calibrated bayesian
Dawid, A. P. (1982) · 1982
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Adaptive mixtures of local experts
Jacobs, R. A., Jordan, M. I., Nowlan, S. J., and Hinton, G. E. (1991) · 1991
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Toward efficient agnostic learning
Kearns, M. J., Schapire, R. E., and Sellie, L. M. (1994) · 1994
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Asymptotic calibration
Foster, D. P. and Vohra, R. V. (1998) · 1998
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Loss functions for binary class probability estimation and classification: Structure and applications
Buja, A., Stuetzle, W., and Shen, Y. (2005) · 2005
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Sample complexity of uniform convergence for multicalibration
Shabat, E., Cohen, L., and Mansour, Y. (2020) · 2005
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Moment multicalibration for uncertainty estimation
Jung, C., Lee, C., Pai, M. M., Roth, A., and Vohra, R. (2020) · 2008
Cited alongside, same era.
Dwork, C., Kim, M. P., Reingold, O., Rothblum, G. N., and Yona, G. (2020) · 2011
Cited alongside, same era.
Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012) · 2012
Cited alongside, same era.
Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N. (2016) · 2016
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J. and Gebru, T. (2018) · 2018
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Why is my classifier discriminatory?
Chen, I., Johansson, F. D., and Sontag, D. (2018) · 2018
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Kearns, M., Neel, S., Roth, A., and Wu, Z. S. (2018) · 2018
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Trainable calibration measures for neural networks from kernel mean embeddings
Kumar, A., Sarawagi, S., and Jain, U. (2018) · 2018
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Probably approximately metric-fair learning
Rothblum, G. and Yona, G. (2018) · 2018
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Learning from outcomes: Evidence-based rankings
Dwork, C., Kim, M. P., Reingold, O., Rothblum, G. N., and Yona, G. (2019) · 2019
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Kleinberg, J., Mullainathan, S., and Raghavan, M. (2016) · 2016
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · 2017
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Decoupled classifiers for fair and efficient machine learning
Dwork, C., Immorlica, N., Kalai, A. T., and Leiserson, M. (2017) · 2017
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Multiaccuracy: Black-box post-processing for fairness in classification
Kim, M. P., Ghorbani, A., and Zou, J. (2019) · 2019
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Online multivalid learning: Means, moments, and prediction intervals
Gupta, V., Jung, C., Noarov, G., Pai, M. M., and Roth, A. (2021) · 2021
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