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Adding constraint support in Machine Learning has the potential to address outstanding issues in data-driven AI systems, such as safety and fairness.
Alternating projections
Boyd, S.; Dattorro, J.; et al. 2003 · 2003
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Markov logic networks
Richardson, M.; and Domingos, P. 2006 · 2006
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Classifying without discriminating
Kamiran, F.; and Calders, T. 2009 · 2009
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Three naive Bayes approaches for discrimination-free classification
Calders, T.; and Verwer, S. 2010 · 2010
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Discrimination aware decision tree learning
Kamiran, F.; Calders, T.; and Pechenizkiy, M. 2010 · 2010
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Data preprocessing techniques for classification without discrimination
Kamiran, F.; and Calders, T. 2011 · 2011
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k-NN as an implementation of situation testing for discrimination discovery and prevention
Luong, B. T.; Ruggieri, S.; and Turini, F. 2011 · 2011
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Scikit-learn: Machine Learning in Python
Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Vanderplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; Perrot, M.; and Duchesnay, E. 2011 · 2011
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Fairness through awareness
Dwork, C.; Hardt, M.; Pitassi, T.; Reingold, O.; and Zemel, R. 2012 · 2012
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Learning fair representations
Zemel, R.; Wu, Y.; Swersky, K.; Pitassi, T.; and Dwork, C. 2013 · 2013
Cited alongside, same era.
Proximal algorithms
Parikh, N.; Boyd, S.; et al. 2014 · 2014
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Semantic-based regularization for learning and inference
Diligenti, M.; Gori, M.; and Saccà, C. 2017 · 2015
Cited alongside, same era.
A confidence-based approach for balancing fairness and accuracy
Fish, B.; Kun, J.; and Lelkes, Á. D. 2016 · 2016
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Equality of opportunity in supervised learning
Hardt, M.; Price, E.; and Srebro, N. 2016 · 2016
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Efficient piecewise training of deep structured models for semantic segmentation
Lin, G.; Shen, C.; Van Den Hengel, A.; and Reid, I. 2016 · 2016
Cited alongside, same era.
Semantic-based regularization for learning and inference
Diligenti, M.; Gori, M.; and Sacca, C. 2017 · 2017
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UCI Machine Learning Repository
Dua, D.; and Graff, C. 2017 · 2017
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End-to-end differentiable proving
Rocktäschel, T.; and Riedel, S. 2017 · 2017
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A new projection method for finding the closest point in the intersection of convex sets
Artacho, F. J. A.; and Campoy, R. 2018 · 2018
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DeepProbLog: Neural Probabilistic Logic Programming
Manhaeve, R.; Dumančić, S.; Kimmig, A.; Demeester, T.; and De Raedt, L. 2018 · 2018
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Learning optimal and fair decision trees for non-discriminative decision-making
Aghaei, S.; Azizi, M. J.; and Vayanos, P. 2019 · 2019
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End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
Ma, X.; and Hovy, E. 2016 · 2016
Cited alongside, same era.
Logic tensor networks: Deep learning and logical reasoning from data and knowledge
Serafini, L.; and Garcez, A. d. 2016 · 2016
Cited alongside, same era.
A Convex Framework for Fair Regression
Berk, R.; Heidari, H.; Jabbari, S.; Joseph, M.; Kearns, M. J.; Morgenstern, J.; Neel, S.; and Roth, A. 2017 · 2017
Cited alongside, same era.
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Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
Cotter, A.; Jiang, H.; Gupta, M. R.; Wang, S.; Narayan, T.; You, S.; and Sridharan, K. 2019 · 2019
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Semi-Supervised Learning using Differentiable Reasoning
Van Krieken, E.; Acar, E.; and Van Harmelen, F. 2019 · 2019
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