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Considerable research effort has been guided towards algorithmic fairness but there is still no major breakthrough.
Learning Non-Discriminatory Predictors
Woodworth, B., Gunasekar, S., Ohannessian, M. I., and Srebro, N. (2017) · 1953
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On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown
Lilliefors, H. W. (1967) · 1967
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Estimation of the Probability of an Event as a Function of Several Independent Variables
Walker, S. H. and Duncan, D. B. (1967) · 1967
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Manuale di economica politica, societa editrice libraria
Pareto, V. (1906) · 1971
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Classification and regression trees
Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A. (1984) · 1984
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Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid
Kohavi, R. (1996) · 1996
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Random Forests
Breiman, L. (2001) · 2001
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Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems
Even-Bar, E., Mannor, S., and Mansour, Y. (2006) · 2006
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Best Arm Identification in Multi-Armed Bandits
Audibert, J.-Y. and Bubeck, S. (2010) · 2010
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Zemel, R. S., Wu, Y., Swersky, K., Pitassi, T., and Dwork, C. (2013) · 2010
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Sequential model-based optimization for general algorithm configuration
Hutter, F., Hoos, H. H., and Leyton-Brown, K. (2011) · 2011
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Random search for hyper-parameter optimization
Bergstra, J. and Bengio, Y. (2012) · 2012
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PAC subset selection in stochastic multi-armed bandits
Kalyanakrishnan, S., Tewari, A., Auer, P., and Stone, P. (2012) · 2012
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Almost optimal exploration in multi-armed bandits
Karnin, Z., Koren, T., and Somekh, O. (2013) · 2013
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Multi-objective Optimization
Deb, K. (2014) · 2014
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Bayesian optimization with inequality constraints
Gardner, J., Kusner, M., Zhixiang, Weinberger, K., and Cunningham, J. (2014) · 2014
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Bayesian optimization with unknown constraints
Gelbart, M. A., Snoek, J., and Adams, R. P. (2014) · 2014
Cited alongside, same era.
lil’ UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits
Jamieson, K., Malloy, M., Nowak, R., and Bubeck, S. (2014) · 2014
Cited alongside, same era.
Predicting excitement at donorschoose.org - kdd cup 2014
Wijesinghe, D. B., Paraneetharan, S., Gogulan, T., and Ahiladas, B. (2014) · 2014
Cited alongside, same era.
Big data’s disparate impact
Barocas, S. and Selbst, A. D. (2016) · 2016
Cited alongside, same era.
Censoring Representations with an Adversary
Edwards, H. and Storkey, A. (2016) · 2016
Cited alongside, same era.
A confidence-based approach for balancing fairness and accuracy
Fish, B., Kun, J., and Lelkes, Á. D. (2016) · 2016
Cited alongside, same era.
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
Buolamwini, J. and Gebru, T. (2018) · 2018
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Two-Player Games for Efficient Non-Convex Constrained Optimization
Cotter, A., Jiang, H., and Sridharan, K. (2018) · 2018
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BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Falkner, S., Klein, A., and Hutter, F. (2018) · 2018
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Algorithmic fairness
Kleinberg, J., Ludwig, J., Mullainathan, S., and Rambachan, A. (2018) · 2018
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Aequitas: A bias and fairness audit toolkit
Saleiro, P., Kuester, B., Stevens, A., Anisfeld, A., Hinkson, L., London, J., and Ghani, R. (2018) · 2018
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Consumer-Lending Discrimination in the FinTech Era
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Equality of opportunity in supervised learning
Hardt, M., Price, E., Price, E., and Srebro, N. (2016) · 2016
Cited alongside, same era.
Non-stochastic best arm identification and hyperparameter optimization
Jamieson, K. and Talwalkar, A. (2016) · 2016
Cited alongside, same era.
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A. (2016) · 2016
Cited alongside, same era.
Taking the human out of the loop: A review of Bayesian optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., and De Freitas, N. (2016) · 2016
Cited alongside, same era.
Artificial intelligence and machine learning in financial services
Board, F. S. (2017) · 2017
Cited alongside, same era.
Optimized pre-processing for discrimination prevention
Calmon, F., Wei, D., Vinzamuri, B., Ramamurthy, K. N., and Varshney, K. R. (2017) · 2017
Cited alongside, same era.
Bartlett, R., Morse, A., Stanton, R., and Wallace, N. (2019) · 2019
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Flexibly Fair Representation Learning by Disentanglement
Creager, E., Madras, D., Jacobsen, J.-H. H., Weis, M. A., Swersky, K., Pitassi, T., and Zemel, R. (2019) · 2019
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A comparative study of fairness-enhancing interventions in machine learning
Friedler, S. A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E. P., and Roth, D. (2019) · 2019
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Automated Machine Learning: Methods, Systems, Challenges
Hutter, F., Kotthoff, L., and Vanschoren, J. (2019) · 2019
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Machine Learning in Medicine
Rajkomar, A., Dean, J., and Kohane, I. (2019) · 2019
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A framework for understanding unintended consequences of machine learning
Suresh, H. and Guttag, J. V. (2019) · 2019
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Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks
Angwin, J., Larson, J., Kirchner, L., and Mattu, S. (2016) · 2020
Closest in time.
Fair bayesian optimization
Perrone, V., Donini, M., Kenthapadi, K., and Archambeau, C. (2020) · 2020
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Chapter 11: Bias and Fairness
Rodolfa, K. T., Saleiro, P., and Ghani, R. (2020) · 2020
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Dealing with bias and fairness in data science systems: A practical hands-on tutorial
Saleiro, P., Rodolfa, K. T., and Ghani, R. (2020) · 2020
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