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Outlier detection and novelty detection are two important topics for anomaly detection.
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Charu C Aggarwal and Philip S Yu · 2001
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Olivier Bousquet and André Elisseeff · 2002
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Novelty detection: a review—part 1: statistical approaches
Markos Markou and Sameer Singh · 2003
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Novelty detection: a review—part 2:: neural network based approaches
Markos Markou and Sameer Singh · 2003
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Victoria Hodge and Jim Austin · 2004
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Heiko Hoffmann · 2007
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Cynthia Dwork · 2008
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Detecting large-scale system problems by mining console logs
Wei Xu, Ling Huang, Armando Fox, David Patterson, and Michael I Jordan · 2009
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Cluster-based outlier detection
Lian Duan, Lida Xu, Ying Liu, and Jun Lee · 2009
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Mining invariants from console logs for system problem detection
Jian-Guang Lou, Qiang Fu, Shengqi Yang, Ye Xu, and Jiang Li · 2010
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Semi-supervised novelty detection
Gilles Blanchard, Gyemin Lee, and Clayton Scott · 2010
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Robust pca via outlier pursuit
Huan Xu, Constantine Caramanis, and Sujay Sanghavi · 2010
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
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Cynthia Dwork · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Arthur Zimek, Erich Schubert, and Hans-Peter Kriegel · 2012
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Frank De Morsier, Devis Tuia, Maurice Borgeaud, Volker Gass, and Jean-Philippe Thiran · 2013
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Cynthia Dwork, Aaron Roth, et al · 2014
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Ensembles for unsupervised outlier detection: challenges and research questions a position paper
Arthur Zimek, Ricardo JGB Campello, and Jörg Sander · 2014
Autoperf: A generalized zero-positive learning system to detect software performance anomalies
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The secret sharer: Measuring unintended neural network memorization & extracting secrets
Nicholas Carlini, Chang Liu, Jernej Kos, Úlfar Erlingsson, and Dawn Song · 2018
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Certified robustness to adversarial examples with differential privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
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Sensitivity and specificity — Wikipedia, the free encyclopedia, 2019
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Differentially private analysis of outliers
Rina Okada, Kazuto Fukuchi, and Jun Sakuma · 2015
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Preserving statistical validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Leon Roth · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
Guilherme O Campos, Arthur Zimek, Jörg Sander, Ricardo JGB Campello, Barbora Micenková, Erich Schubert, Ira Assent, and Michael E Houle · 2016
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Machine learning models that remember too much
Congzheng Song, Thomas Ristenpart, and Vitaly Shmatikov · 2017
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Deeplog: Anomaly detection and diagnosis from system logs through deep learning
Min Du, Feifei Li, Guineng Zheng, and Vivek Srikumar · 2017
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Wikipedia · 2019
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Precision and recall — Wikipedia, the free encyclopedia, 2019
Wikipedia · 2019
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sklearn.metrics.average_precision_score, 2017
scikit-learn · 2019
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sklearn.metrics.roc_auc_score, 2017
scikit-learn · 2019
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notmnist dataset used in udacity’s deep learning mooc, 2017
Kaggle · 2019
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Hdfs log dataset, 2009
Wei Xu · 2019
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Novelty and outlier detection, 2017
scikit-learn · 2019
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Preserving differential privacy in adversarial learning with provable robustness
NhatHai Phan, Ruoming Jin, My T Thai, Han Hu, and Dejing Dou · 2019
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Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan and Vitaly Shmatikov · 2019
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