Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data

LOF: Identifying density-based local outliers

Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander. 2000 · 2000

Earlier work this paper cites.

Algorithms for Inverse Reinforcement Learning. In ICML . Citeseer

Andrew Y Ng and Stuart Russell. 2000 · 2000

Earlier work this paper cites.

Support vector data description

David MJ Tax and Robert PW Duin. 2004 · 2004

Earlier work this paper cites.

Outlier detection by active learning. In KDD . 504–509

Naoki Abe, Bianca Zadrozny, and John Langford. 2006 · 2006

Earlier work this paper cites.

Learning classifiers from only positive and unlabeled data. In KDD . ACM, 213–220

Charles Elkan and Keith Noto. 2008 · 2008

Earlier work this paper cites.

Rectified linear units improve restricted boltzmann machines. In ICML . 807–814

Vinod Nair and Geoffrey E Hinton. 2010 · 2010

Earlier work this paper cites.

Fast and reliable anomaly detection in categorical data. In CIKM . 415–424

Leman Akoglu, Hanghang Tong, Jilles Vreeken, and Christos Faloutsos. 2012 · 2012

Earlier work this paper cites.

Isolation-based anomaly detection

Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2012 · 2012

Earlier work this paper cites.

Area under the precision-recall curve: point estimates and confidence intervals. In ECML/PKDD . 451–466

Kendrick Boyd, Kevin H Eng, and C David Page. 2013 · 2013

Earlier work this paper cites.

Toward supervised anomaly detection

Nico Görnitz, Marius Kloft, Konrad Rieck, and Ulf Brefeld. 2013 · 2013

Earlier work this paper cites.

Guilt by association: Large scale malware detection by mining file-relation graphs. In KDD . 1524–1533

Acar Tamersoy, Kevin Roundy, and Duen Horng Chau. 2014 · 2014

Earlier work this paper cites.

Human-level control through deep reinforcement learning

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015

Cited alongside, same era.

Outlier analysis

Charu C Aggarwal. 2017 · 2017

Cited alongside, same era.

Curiosity-driven Exploration by Self-supervised Prediction. In ICML . 2778–2787

Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell. 2017 · 2017

Cited alongside, same era.

Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In IPMI . Springer, Cham, 146–157

Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs. 2017 · 2017

Cited alongside, same era.

Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors

Kai Ming Ting, Takashi Washio, Jonathan R Wells, and Sunil Aryal. 2017 · 2017

Cited alongside, same era.

Recommendations with negative feedback via pairwise deep reinforcement learning. In KDD . 1040–1048

Xiangyu Zhao, Liang Zhang, Zhuoye Ding, Long Xia, Jiliang Tang, and Dawei Yin. 2018 · 2018

Later among the works it cites.

DRN: A deep reinforcement learning framework for news recommendation. In WWW . 167–176

Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, and Zhenhui Li. 2018 · 2018

Later among the works it cites.

Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In ICLR

Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. 2018 · 2018

Later among the works it cites.

Sequential Anomaly Detection using Inverse Reinforcement Learning. In KDD . 1480–1490

Min-hwan Oh and Garud Iyengar. 2019 · 2019

Later among the works it cites.

Deep weakly-supervised anomaly detection

Original

Guansong Pang, Chunhua Shen, Huidong Jin, and Anton van den Hengel. 2019b · 2019