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Multi-group agnostic learning is a formal learning criterion that is concerned with the conditional risks of predictors within subgroups of a population.
On tail probabilities for martingales
Freedman, D · 1975
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The strength of weak learnability
Schapire, R. E · 1990
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Boosting a weak learning algorithm by majority
Freund, Y · 1995
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On weak learning
Helmbold, D. P. and Warmuth, M. K · 1995
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Empirical support for winnow and weighted-majority algorithms: Results on a calendar scheduling domain
Blum, A · 1997
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Using and combining predictors that specialize
Freund, Y., Schapire, R. E., Singer, Y., and Warmuth, M. K · 1997
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Improving predictive inference under covariate shift by weighting the log-likelihood function
Shimodaira, H · 2000
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On the generalization ability of on-line learning algorithms
Cesa-Bianchi, N., Conconi, A., and Gentile, C · 2004
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Learning and evaluating classifiers under sample selection bias
Zadrozny, B · 2004
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Theory of classification: A survey of some recent advances
Boucheron, S., Bousquet, O., and Lugosi, G · 2005
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From external to internal regret
Blum, A. and Mansour, Y · 2007
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Robust optimization
Ben-Tal, A., El Ghaoui, L., and Nemirovski, A · 2009
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Regularity, boosting, and efficiently simulating every high-entropy distribution
Trevisan, L., Tulsiani, M., and Vadhan, S · 2009
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A theory of learning from different domains
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., and Vaughan, J. W · 2010
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Learning bounds for importance weighting
Cortes, C., Mansour, Y., and Mohri, M · 2010
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Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R · 2012
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A second-order bound with excess losses
Gaillard, P., Stoltz, G., and Van Erven, T · 2014
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Understanding machine learning: From theory to algorithms
Shalev-Shwartz, S. and Ben-David, S · 2014
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Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N · 2016
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A reductions approach to fair classification
Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., and Wallach, H · 2018
Cited alongside, same era.
Regularized learning for domain adaptation under label shifts
Azizzadenesheli, K., Liu, A., Yang, F., and Anandkumar, A · 2018
Cited alongside, same era.
Multiaccuracy: Black-box post-processing for fairness in classification
Kim, M., Ghorbani, A., and Zou, J · 2019
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Agnostic federated learning
Mohri, M., Sivek, G., and Suresh, A. T · 2019
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Advancing subgroup fairness via sleeping experts
Blum, A. and Lykouris, T · 2020
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Distributionally robust losses for latent covariate mixtures, 2020
Duchi, J., Hashimoto, T., and Namkoong, H · 2020
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Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
Oakden-Rayner, L., Dunnmon, J., Carneiro, G., and Ré, C · 2020
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No subclass left behind: Fine-grained robustness in coarse-grained classification problems
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Data-driven robust optimization
Bertsimas, D., Gupta, V., and Kallus, N · 2018
Cited alongside, same era.
Empirical risk minimization under fairness constraints
Donini, M., Oneto, L., Ben-David, S., Shawe-Taylor, J., and Pontil, M · 2018
Cited alongside, same era.
Individual fairness under composition
Dwork, C. and Ilvento, C · 2018
Cited alongside, same era.
Online learning with an unknown fairness metric
Gillen, S., Jung, C., Kearns, M., and Roth, A · 2018
Cited alongside, same era.
Multicalibration: Calibration for the (computationally-identifiable) masses
Hébert-Johnson, U., Kim, M., Reingold, O., and Rothblum, G · 2018
Cited alongside, same era.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Kearns, M., Neel, S., Roth, A., and Wu, Z. S · 2018
Cited alongside, same era.
Sohoni, N. S., Dunnmon, J. A., Angus, G., Gu, A., and Ré, C · 2020
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Multiaccurate proxies for downstream fairness
Diana, E., Gill, W., Kearns, M., Kenthapadi, K., Roth, A., and Sharifi-Malvajerdi, S · 2021
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Statistics of robust optimization: A generalized empirical likelihood approach
Duchi, J. C., Glynn, P. W., and Namkoong, H · 2021
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Outcome indistinguishability
Dwork, C., Kim, M. P., Reingold, O., Rothblum, G. N., and Yona, G · 2021
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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
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Realizable learning is all you need
Hopkins, M., Kane, D., Lovett, S., and Mahajan, G · 2021
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Just train twice: Improving group robustness without training group information
Liu, E. Z., Haghgoo, B., Chen, A. S., Raghunathan, A., Koh, P. W., Sagawa, S., Liang, P., and Finn, C · 2021
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Online multiobjective minimax optimization and applications
Noarov, G., Pai, M., and Roth, A · 2021
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Beyond the frontier: Fairness without privacy loss
Globus-Harris, I., Kearns, M., and Roth, A · 2022
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