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[1] Zhe Zhao,et al. Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations , 2017, ArXiv.
[2] Krishna P. Gummadi,et al. Learning Fair Classifiers , 2015, 1507.05259.
[3] Jon M. Kleinberg,et al. On Fairness and Calibration , 2017, NIPS.
[4] Nathan Kallus,et al. The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric , 2019, NeurIPS.
[5] Cynthia Dwork,et al. Fairness Under Composition , 2018, ITCS.
[6] Bernhard Schölkopf,et al. Kernel Mean Embedding of Distributions: A Review and Beyonds , 2016, Found. Trends Mach. Learn..
[7] Harikrishna Narasimhan,et al. Pairwise Fairness for Ranking and Regression , 2019, AAAI.
[8] Nathan Srebro,et al. Equality of Opportunity in Supervised Learning , 2016, NIPS.
[9] Ricardo Baeza-Yates,et al. FA*IR: A Fair Top-k Ranking Algorithm , 2017, CIKM.
[10] Max Welling,et al. The Variational Fair Autoencoder , 2015, ICLR.
[11] James Y. Zou,et al. Multiaccuracy: Black-Box Post-Processing for Fairness in Classification , 2018, AIES.
[12] Michael D. Ekstrand,et al. Exploring author gender in book rating and recommendation , 2018, User Modeling and User-Adapted Interaction.
[13] Jaana Kekäläinen,et al. Cumulated gain-based evaluation of IR techniques , 2002, TOIS.
[14] Sahin Cem Geyik,et al. Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search , 2019, KDD.
[15] Sreenivas Gollapudi,et al. An axiomatic approach for result diversification , 2009, WWW '09.
[16] Ed H. Chi,et al. Fairness in Recommendation Ranking through Pairwise Comparisons , 2019, KDD.
[17] Jade Goldstein-Stewart,et al. The use of MMR, diversity-based reranking for reordering documents and producing summaries , 1998, SIGIR '98.
[18] Blake Lemoine,et al. Mitigating Unwanted Biases with Adversarial Learning , 2018, AIES.
[19] Thorsten Joachims,et al. Fairness of Exposure in Rankings , 2018, KDD.
[20] Jure Leskovec,et al. Learning Attitudes and Attributes from Multi-aspect Reviews , 2012, 2012 IEEE 12th International Conference on Data Mining.
[21] Zhe Zhao,et al. Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts , 2018, KDD.
[22] Joaquin Quiñonero Candela,et al. Practical Lessons from Predicting Clicks on Ads at Facebook , 2014, ADKDD'14.
[23] Toniann Pitassi,et al. Learning Adversarially Fair and Transferable Representations , 2018, ICML.
[24] Filip Radlinski,et al. Learning diverse rankings with multi-armed bandits , 2008, ICML '08.
[25] H. B. Mann,et al. On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .
[26] Toon Calders,et al. Building Classifiers with Independency Constraints , 2009, 2009 IEEE International Conference on Data Mining Workshops.
[27] John Langford,et al. A Reductions Approach to Fair Classification , 2018, ICML.
[28] Sreenivas Gollapudi,et al. Diversifying search results , 2009, WSDM '09.
[29] Filip Radlinski,et al. Learning optimally diverse rankings over large document collections , 2010, ICML.
[30] Adam Tauman Kalai,et al. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings , 2016, NIPS.
[31] Indre Zliobaite,et al. On the relation between accuracy and fairness in binary classification , 2015, ArXiv.
[32] Fabrizio Silvestri,et al. Efficient Diversification of Web Search Results , 2011, Proc. VLDB Endow..
[33] Jimmy J. Lin,et al. A cascade ranking model for efficient ranked retrieval , 2011, SIGIR.
[34] Maya R. Gupta,et al. Satisfying Real-world Goals with Dataset Constraints , 2016, NIPS.
[35] Jade Goldstein-Stewart,et al. The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries , 1998, SIGIR Forum.
[36] Krishna P. Gummadi,et al. Fairness Constraints: Mechanisms for Fair Classification , 2015, AISTATS.
[37] Suju Rajan,et al. Beyond clicks: dwell time for personalization , 2014, RecSys '14.
[38] AdomaviciusGediminas,et al. Toward the Next Generation of Recommender Systems , 2005 .
[39] Maya R. Gupta,et al. Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints , 2018, ICML.
[40] Bernhard Schölkopf,et al. A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..
[41] C. Dwork,et al. Group Fairness Under Composition , 2018 .
[42] Lucy Vasserman,et al. Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification , 2019, WWW.
[43] Robin D. Burke,et al. Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.
[44] Allison Woodruff,et al. Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements , 2019, AIES.
[45] Vaibhava Goel,et al. McGan: Mean and Covariance Feature Matching GAN , 2017, ICML.
[46] Gediminas Adomavicius,et al. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.