Fetching the paper…
Reading the bibliography…
Fairness and Outlier Detection (OD) are closely related, as it is exactly the goal of OD to spot rare, minority samples in a given population.
LOF: identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data . 93–104
Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander. 2000 · 2000
Earlier work this paper cites.
Estimating the support of a high-dimensional distribution
Bernhard Schölkopf, John C Platt, John Shawe-Taylor, Alex J Smola, and Robert C Williamson. 2001 · 2001
Earlier work this paper cites.
Cumulated gain-based evaluation of IR techniques
K. Järvelin and J. Kekäläinen. 2002 · 2002
Earlier work this paper cites.
Automatic change detection in multimodal serial MRI: application to multiple sclerosis lesion evolution
Marcel Bosc, Fabrice Heitz, Jean-Paul Armspach, Izzie Namer, Daniel Gounot, and Lucien Rumbach. 2003 · 2003
Earlier work this paper cites.
Deepak P and Savitha Sam Abraham. 2020 · 2005
Earlier work this paper cites.
Anomaly based network intrusion detection with unsupervised outlier detection. In 2006 IEEE International Conference on Communications , Vol. 5. IEEE, 2388–2393
Jiong Zhang and Mohammad Zulkernine. 2006 · 2006
Earlier work this paper cites.
Anomaly detection: A survey
Varun Chandola, Arindam Banerjee, and Vipin Kumar. 2009 · 2009
Earlier work this paper cites.
Learning from imbalanced data
Haibo He and Edwardo A Garcia. 2009 · 2009
Earlier work this paper cites.
Luck Egalitarianism: Equality, Responsibility, and Justice
Carl Knight. 2009 · 2009
Earlier work this paper cites.
Unsupervised DRG upcoding detection in healthcare databases. In 2010 IEEE ICDM Workshops . IEEE, 600–605
Wei Luo and Marcus Gallagher. 2010 · 2010
Earlier work this paper cites.
A comprehensive survey of data mining-based fraud detection research
Clifton Phua, Vincent Lee, Kate Smith, and Ross Gayler. 2010 · 2010
Earlier work this paper cites.
A general approximation framework for direct optimization of information retrieval measures
Tao Qin, Tie-Yan Liu, and Hang Li. 2010 · 2010
Earlier work this paper cites.
A survey of outlier detection methods in network anomaly identification
Prasanta Gogoi, DK Bhattacharyya, Bhogeswar Borah, and Jugal K Kalita. 2011 · 2011
Earlier work this paper cites.
Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders. 2012 · 2012
Earlier work this paper cites.
Outlier detection for temporal data: A survey
Manish Gupta, Jing Gao, Charu C Aggarwal, and Jiawei Han. 2013 · 2013
Earlier work this paper cites.
UCI machine learning repository
Moshe Lichman et al · 2013
Earlier work this paper cites.
Parallel auto-encoder for efficient outlier detection. In 2013 IEEE International Conference on Big Data . IEEE, 15–17
Yunlong Ma, Peng Zhang, Yanan Cao, and Li Guo. 2013 · 2013
Earlier work this paper cites.
Learning fair representations. In ICML . 325–333
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. 2013 · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Spotting suspicious link behavior with fbox: An adversarial perspective. In 2014 IEEE International Conference on Data Mining . IEEE, 959–964
Neil Shah, Alex Beutel, Brian Gallagher, and Christos Faloutsos. 2014 · 2014
Earlier work this paper cites.
Outlier analysis. In Data mining . Springer, 237–263
Charu C Aggarwal. 2015 · 2015
Cited alongside, same era.
Variational autoencoder based anomaly detection using reconstruction probability
Jinwon An and Sungzoon Cho. 2015 · 2015
Cited alongside, same era.
Censoring representations with an adversary
Harrison Edwards and Amos Storkey. 2015 · 2015
Cited alongside, same era.
Certifying and removing disparate impact. In proceedings of the 21th ACM SIGKDD . 259–268
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. 2015 · 2015
Cited alongside, same era.
The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel. 2015 · 2015
Cited alongside, same era.
Adaptive sensitive reweighting to mitigate bias in fairness-aware classification. In Proceedings of the 2018 World Wide Web Conference . 853–862
Emmanouil Krasanakis, Eleftherios Spyromitros-Xioufis, Symeon Papadopoulos, and Yiannis Kompatsiaris. 2018 · 2018
Later among the works it cites.
Does mitigating ML’s impact disparity require treatment disparity?. In Advances in Neural Information Processing Systems . 8125–8135
Zachary Lipton, Julian McAuley, and Alexandra Chouldechova. 2018 · 2018
Later among the works it cites.
Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel. 2018 · 2018
Later among the works it cites.
Deep One-Class Classification. In Proceedings of the 35th International Conference on Machine Learning , Vol. 80. 4393–4402
Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Lucas Deecke, Shoaib A. Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft. 2018 · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions
Véronique Van Vlasselaer, Cristián Bravo, Olivier Caelen, Tina Eliassi-Rad, Leman Akoglu, Monique Snoeck, and Bart Baesens. 2015 · 2015
Cited alongside, same era.
Demographic dialectal variation in social media: A case study of African-American English
Su Lin Blodgett, Lisa Green, and Brendan O’Connor. 2016 · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning. In Advances in neural information processing systems . 3315–3323
Moritz Hardt, Eric Price, and Nati Srebro. 2016 · 2016
Cited alongside, same era.
Fairness in machine learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2017 · 2017
Cited alongside, same era.
Data decisions and theoretical implications when adversarially learning fair representations
Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H Chi. 2017 · 2017
Cited alongside, same era.
Outlier detection with autoencoder ensembles. In Proceedings of the 2017 SIAM international conference on data mining . SIAM, 90–98
Jinghui Chen, Saket Sathe, Charu Aggarwal, and Deepak Turaga. 2017 · 2017
Cited alongside, same era.
On convergence and stability of gans
Naveen Kodali, Jacob Abernethy, James Hays, and Zsolt Kira. 2017 · 2017
Cited alongside, same era.
Fairness definitions explained. In 2018 IEEE/ACM International Workshop on Software Fairness (FairWare) . IEEE, 1–7
Sahil Verma and Julia Rubin. 2018 · 2018
Later among the works it cites.
Mitigating unwanted biases with adversarial learning. In AIES . 335–340
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. 2018 · 2018
Later among the works it cites.
One-network adversarial fairness. In AAAI , Vol. 33. 2412–2420
Tameem Adel, Isabel Valera, Zoubin Ghahramani, and Adrian Weller. 2019 · 2019
Later among the works it cites.
Fairness and Machine Learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2019 · 2019
Later among the works it cites.
Putting fairness principles into practice: Challenges, metrics, and improvements. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society . 453–459
Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Allison Woodruff, Christine Luu, Pierre Kreitmann, Jonathan Bischof, and Ed H Chi. 2019 · 2019
Later among the works it cites.
Medicare fraud detection using neural networks
Justin M Johnson and Taghi M Khoshgoftaar. 2019 · 2019
Later among the works it cites.
A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2019 · 2019
Later among the works it cites.
Convex formulations for fair principal component analysis. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 663–670
Matt Olfat and Anil Aswani. 2019 · 2019
Later among the works it cites.
George Cevora. 2020 · 2020
Closest in time.
AutoAudit: Mining Accounting and Time-Evolving Graphs
Meng-Chieh Lee, Yue Zhao, Aluna Wang, Pierre Jinghong Liang, Leman Akoglu, Vincent S Tseng, and Christos Faloutsos. 2020 · 2020
Closest in time.
Deep learning for anomaly detection: A review
Guansong Pang, Chunhua Shen, Longbing Cao, and Anton van den Hengel. 2020 · 2020
Closest in time.
Anomaly-based intrusion detection from network flow features using variational autoencoder
Sultan Zavrak and Murat İskefiyeli. 2020 · 2020
Closest in time.
Towards Fair Deep Anomaly Detection
Hongjing Zhang and Ian Davidson. 2020 · 2020
Closest in time.
A framework for determining the fairness of outlier detection. In Proceedings of the 24th European Conference on Artificial Intelligence (ECAI2020) , Vol. 2029
Ian Davidson and Selvan Suntiha Ravi. 2020a · 2029
Closest in time.
A framework for determining the fairness of outlier detection. In Proceedings of the 24th European Conference on Artificial Intelligence (ECAI2020) , Vol. 2029
Ian Davidson and Selvan Suntiha Ravi. 2020b · 2029
Closest in time.