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The notion of omnipredictors (Gopalan, Kalai, Reingold, Sharan and Wieder ITCS 2021), suggested a new paradigm for loss minimization.
Efficient distribution-free learning of probabilistic concepts
M. Kearns and R. Schapire · 1990
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On agnostic boosting and parity learning
A. T. Kalai, Y. Mansour, and E. Verbin · 2008
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Distribution-specific agnostic boosting
V. Feldman · 2010
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Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
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Fairness-aware classifier with prejudice remover regularizer
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma · 2012
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R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
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Satisfying real-world goals with dataset constraints
G. Goh, A. Cotter, M. Gupta, and M. P. Friedlander · 2016
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Equality of opportunity in supervised learning
M. Hardt, E. Price, and N. Srebro · 2016
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A reductions approach to fair classification
A. Agarwal, A. Beygelzimer, M. Dudík, J. Langford, and H. Wallach · 2018
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Empirical risk minimization under fairness constraints
M. Donini, L. Oneto, S. Ben-David, J. S. Shawe-Taylor, and M. Pontil · 2018
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Multicalibration: Calibration for the (computationally-identifiable) masses
U. Hébert-Johnson, M. Kim, O. Reingold, and G. Rothblum · 2018
Cited alongside, same era.
Fairness through computationally-bounded awareness
M. Kim, O. Reingold, and G. Rothblum · 2018
Cited alongside, same era.
Learning with complex loss functions and constraints
H. Narasimhan · 2018
Cited alongside, same era.
Probably approximately metric-fair learning
G. N. Rothblum and G. Yona · 2018
Cited alongside, same era.
Classification with fairness constraints: A meta-algorithm with provable guarantees
L. E. Celis, L. Huang, V. Keswani, and N. K. Vishnoi · 2019
Cited alongside, same era.
Optimization with non-differentiable constraints with applications to fairness, recall, churn, and other goals
A. Cotter, H. Jiang, M. R. Gupta, S. Wang, T. Narayan, S. You, and K. Sridharan · 2019
A short note on learning discrete distributions
C. L. Canonne · 2020
Later among the works it cites.
Outcome indistinguishability
C. Dwork, M. P. Kim, O. Reingold, G. N. Rothblum, and G. Yona · 2021
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Multi-group agnostic PAC learnability
G. N. Rothblum and G. Yona · 2021
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Individually fair learning with one-sided feedback
Y. Bechavod and A. Roth · 2022
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Multicalibrated regression for downstream fairness
I. Globus-Harris, V. Gupta, C. Jung, M. Kearns, J. Morgenstern, and A. Roth · 2022
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Omnipredictors
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Cited alongside, same era.
Learning from outcomes: Evidence-based rankings
C. Dwork, M. P. Kim, O. Reingold, G. N. Rothblum, and G. Yona · 2019
Cited alongside, same era.
Multiaccuracy: Black-box post-processing for fairness in classification
M. P. Kim, A. Ghorbani, and J. Zou · 2019
Cited alongside, same era.
Learning optimal fair policies
R. Nabi, D. Malinsky, and I. Shpitser · 2019
Cited alongside, same era.
Average individual fairness: Algorithms, generalization and experiments
S. Sharifi-Malvajerdi, M. Kearns, and A. Roth · 2019
Cited alongside, same era.
Metric entropy duality and the sample complexity of outcome indistinguishability
L. Hu, C. Peale, and O. Reingold
Cited in the paper.
Provably fair federated learning via bounded group loss
S. Hu, Z. S. Wu, and V. Smith
Cited in the paper.
P. Gopalan, A. T. Kalai, O. Reingold, V. Sharan, and U. Wieder · 2022
Closest in time.
Universal adaptability: Target-independent inference that competes with propensity scoring
M. P. Kim, C. Kern, S. Goldwasser, F. Kreuter, and O. Reingold · 2022
Closest in time.
Loss Minimization Through the Lens Of Outcome Indistinguishability
P. Gopalan, L. Hu, M. P. Kim, O. Reingold, and U. Wieder · 2023
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Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes
L. Hu and C. Peale · 2023
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Making Decisions Under Outcome Performativity
M. P. Kim and J. C. Perdomo · 2023
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