\begin{thebibliography}{74}
\providecommand{\natexlab}[1]{#1}
\providecommand{\url}[1]{\texttt{#1}}
\expandafter\ifx\csname urlstyle\endcsname\relax
  \providecommand{\doi}[1]{doi: #1}\else
  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi

\bibitem[Aizenbud and Sober(2021)]{aizenbud2021non}
Yariv Aizenbud and Barak Sober.
\newblock Non-parametric estimation of manifolds from noisy data.
\newblock \emph{arXiv preprint arXiv:2105.04754}, 2021.

\bibitem[Aizenbud et~al.(2015)Aizenbud, Bermanis, and Averbuch]{aizenbud2015pca}
Yariv Aizenbud, Amit Bermanis, and Amir Averbuch.
\newblock Pca-based out-of-sample extension for dimensionality reduction.
\newblock \emph{arXiv preprint arXiv:1511.00831}, 2015.

\bibitem[Angiulli and Pizzuti(2002)]{angiulli2002fast}
Fabrizio Angiulli and Clara Pizzuti.
\newblock Fast outlier detection in high dimensional spaces.
\newblock In \emph{European conference on principles of data mining and knowledge discovery}, pages 15--27. Springer, 2002.

\bibitem[Bergman and Hoshen(2020)]{bergman2020classification}
Liron Bergman and Yedid Hoshen.
\newblock Classification-based anomaly detection for general data.
\newblock \emph{arXiv preprint arXiv:2005.02359}, 2020.

\bibitem[Bishop(1994)]{bishop1994novelty}
Christopher~M Bishop.
\newblock Novelty detection and neural network validation.
\newblock \emph{IEE Proceedings-Vision, Image and Signal processing}, 141\penalty0 (4):\penalty0 217--222, 1994.

\bibitem[Boyd et~al.(2004)Boyd, Boyd, and Vandenberghe]{boyd2004convex}
Stephen Boyd, Stephen~P Boyd, and Lieven Vandenberghe.
\newblock \emph{Convex optimization}.
\newblock Cambridge university press, 2004.

\bibitem[Bregman et~al.(2021)Bregman, Lindenbaum, and Rabin]{bregman2021array}
Y~Bregman, O~Lindenbaum, and N~Rabin.
\newblock Array based earthquakes-explosion discrimination using diffusion maps.
\newblock \emph{Pure and Applied Geophysics}, 178\penalty0 (7):\penalty0 2403--2418, 2021.

\bibitem[Breunig et~al.(2000)Breunig, Kriegel, Ng, and Sander]{breunig2000lof}
Markus~M Breunig, Hans-Peter Kriegel, Raymond~T Ng, and J{\"o}rg Sander.
\newblock Lof: identifying density-based local outliers.
\newblock In \emph{Proceedings of the 2000 ACM SIGMOD international conference on Management of data}, pages 93--104, 2000.

\bibitem[Chawla et~al.(2019)Chawla, Jacob, Lee, and Fallon]{anom-cyber}
Ashima Chawla, Paul Jacob, Brian Lee, and Sheila Fallon.
\newblock Bidirectional lstm autoencoder for sequence based anomaly detection in cyber security.
\newblock \emph{International Journal of Simulation--Systems, Science \& Technology}, 2019.

\bibitem[Chen et~al.(2017)Chen, Sathe, Aggarwal, and Turaga]{chen2017outlier}
Jinghui Chen, Saket Sathe, Charu Aggarwal, and Deepak Turaga.
\newblock Outlier detection with autoencoder ensembles.
\newblock In \emph{Proceedings of the 2017 SIAM international conference on data mining}, pages 90--98. SIAM, 2017.

\bibitem[Chen et~al.(2001)Chen, Zhou, and Huang]{chen2001one}
Yunqiang Chen, Xiang~Sean Zhou, and Thomas~S Huang.
\newblock One-class svm for learning in image retrieval.
\newblock In \emph{Proceedings 2001 International Conference on Image Processing (Cat. No. 01CH37205)}, volume~1, pages 34--37. IEEE, 2001.

\bibitem[Constantinou(2018)]{constantinou2018pynomaly}
Valentino Constantinou.
\newblock Pynomaly: Anomaly detection using local outlier probabilities (loop).
\newblock \emph{Journal of Open Source Software}, 3\penalty0 (30):\penalty0 845, 2018.

\bibitem[Deecke et~al.(2021)Deecke, Ruff, Vandermeulen, and Bilen]{deecke2021transfer}
Lucas Deecke, Lukas Ruff, Robert~A Vandermeulen, and Hakan Bilen.
\newblock Transfer-based semantic anomaly detection.
\newblock In \emph{International Conference on Machine Learning}, pages 2546--2558. PMLR, 2021.

\bibitem[Deng and Li(2022)]{deng2022anomaly}
Hanqiu Deng and Xingyu Li.
\newblock Anomaly detection via reverse distillation from one-class embedding.
\newblock In \emph{Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages 9737--9746, 2022.

\bibitem[Du and Mordatch(2019)]{du2019implicit}
Yilun Du and Igor Mordatch.
\newblock Implicit generation and modeling with energy based models.
\newblock \emph{Advances in Neural Information Processing Systems}, 32, 2019.

\bibitem[Eduardo et~al.(2020)Eduardo, Naz{\'a}bal, Williams, and Sutton]{eduardo2020robust}
Simao Eduardo, Alfredo Naz{\'a}bal, Christopher~KI Williams, and Charles Sutton.
\newblock Robust variational autoencoders for outlier detection and repair of mixed-type data.
\newblock In \emph{International Conference on Artificial Intelligence and Statistics}, pages 4056--4066. PMLR, 2020.

\bibitem[Ghoting et~al.(2008)Ghoting, Parthasarathy, and Otey]{ghoting2008fast}
Amol Ghoting, Srinivasan Parthasarathy, and Matthew~Eric Otey.
\newblock Fast mining of distance-based outliers in high-dimensional datasets.
\newblock \emph{Data Mining and Knowledge Discovery}, 16\penalty0 (3):\penalty0 349--364, 2008.

\bibitem[Goldstein and Dengel(2012)]{HBOS}
Markus Goldstein and Andreas Dengel.
\newblock Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm.
\newblock \emph{KI-2012: Poster and Demo Track}, pages 59--63, 2012.

\bibitem[Goyal et~al.(2020)Goyal, Raghunathan, Jain, Simhadri, and Jain]{goyal2020drocc}
Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha~Vardhan Simhadri, and Prateek Jain.
\newblock Drocc: Deep robust one-class classification.
\newblock In \emph{International Conference on Machine Learning}, pages 3711--3721. PMLR, 2020.

\bibitem[He et~al.(2003)He, Xu, and Deng]{cblof}
Zengyou He, Xiaofei Xu, and Shengchun Deng.
\newblock Discovering cluster-based local outliers.
\newblock \emph{Pattern Recognition Letters}, 24\penalty0 (9-10):\penalty0 1641--1650, 2003.

\bibitem[Hendrycks et~al.(2018)Hendrycks, Mazeika, and Dietterich]{hendrycks2018deep}
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich.
\newblock Deep anomaly detection with outlier exposure.
\newblock \emph{arXiv preprint arXiv:1812.04606}, 2018.

\bibitem[Hendrycks et~al.(2019)Hendrycks, Mazeika, Kadavath, and Song]{hendrycks2019using}
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song.
\newblock Using self-supervised learning can improve model robustness and uncertainty.
\newblock \emph{Advances in Neural Information Processing Systems}, 32, 2019.

\bibitem[Hojjati and Armanfard(2021)]{hojjati2021dasvdd}
Hadi Hojjati and Narges Armanfard.
\newblock Dasvdd: Deep autoencoding support vector data descriptor for anomaly detection.
\newblock \emph{arXiv preprint arXiv:2106.05410}, 2021.

\bibitem[Huber(1987)]{huber1987place}
Peter~J Huber.
\newblock The place of the l1-norm in robust estimation.
\newblock \emph{Computational Statistics \& Data Analysis}, 5\penalty0 (4):\penalty0 255--262, 1987.

\bibitem[Ishii and Takanashi(2019)]{ishii2019low}
Yoshinao Ishii and Masaki Takanashi.
\newblock Low-cost unsupervised outlier detection by autoencoders with robust estimation.
\newblock \emph{Journal of Information Processing}, 27:\penalty0 335--339, 2019.

\bibitem[Ishii et~al.(2020)Ishii, Koide, and Hayakawa]{ishii2020l0}
Yoshinao Ishii, Satoshi Koide, and Keiichiro Hayakawa.
\newblock L0-norm constrained autoencoders for unsupervised outlier detection.
\newblock In \emph{Pacific-Asia Conference on Knowledge Discovery and Data Mining}, pages 674--687. Springer, 2020.

\bibitem[Ishii et~al.(2022)Ishii, Koide, and Hayakawa]{ishii2022learning}
Yoshinao Ishii, Satoshi Koide, and Keiichiro Hayakawa.
\newblock Learning low-dimensional manifolds under the l0-norm constraint for unsupervised outlier detection.
\newblock \emph{International Journal of Data Science and Analytics}, pages 1--15, 2022.

\bibitem[Japkowicz et~al.(1995)Japkowicz, Myers, Gluck, et~al.]{japkowicz1995novelty}
Nathalie Japkowicz, Catherine Myers, Mark Gluck, et~al.
\newblock A novelty detection approach to classification.
\newblock In \emph{IJCAI}, volume~1, pages 518--523. Citeseer, 1995.

\bibitem[Jin et~al.(2001)Jin, Tung, and Han]{jin2001mining}
Wen Jin, Anthony~KH Tung, and Jiawei Han.
\newblock Mining top-n local outliers in large databases.
\newblock In \emph{Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining}, pages 293--298, 2001.

\bibitem[Jin et~al.(2006)Jin, Tung, Han, and Wang]{jin2006ranking}
Wen Jin, Anthony~KH Tung, Jiawei Han, and Wei Wang.
\newblock Ranking outliers using symmetric neighborhood relationship.
\newblock In \emph{Pacific-Asia conference on knowledge discovery and data mining}, pages 577--593. Springer, 2006.

\bibitem[Kriegel et~al.(2008)Kriegel, Schubert, and Zimek]{abod}
Hans-Peter Kriegel, Matthias Schubert, and Arthur Zimek.
\newblock Angle-based outlier detection in high-dimensional data.
\newblock In \emph{Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining}, pages 444--452, 2008.

\bibitem[Kriegel et~al.(2009{\natexlab{a}})Kriegel, Kr{\"o}ger, Schubert, and Zimek]{SOD}
Hans-Peter Kriegel, Peer Kr{\"o}ger, Erich Schubert, and Arthur Zimek.
\newblock Outlier detection in axis-parallel subspaces of high dimensional data.
\newblock In \emph{Pacific-Asia Conference on Knowledge Discovery and Data Mining}, pages 831--838. Springer, 2009{\natexlab{a}}.

\bibitem[Kriegel et~al.(2009{\natexlab{b}})Kriegel, Kr{\"o}ger, Schubert, and Zimek]{kriegel2009loop}
Hans-Peter Kriegel, Peer Kr{\"o}ger, Erich Schubert, and Arthur Zimek.
\newblock Loop: local outlier probabilities.
\newblock In \emph{Proceedings of the 18th ACM conference on Information and knowledge management}, pages 1649--1652, 2009{\natexlab{b}}.

\bibitem[Lai et~al.(2019)Lai, Zou, and Lerman]{lai2019robust}
Chieh-Hsin Lai, Dongmian Zou, and Gilad Lerman.
\newblock Robust subspace recovery layer for unsupervised anomaly detection.
\newblock \emph{arXiv preprint arXiv:1904.00152}, 2019.

\bibitem[LeCun et~al.(1989)]{lecun1989generalization}
Yann LeCun et~al.
\newblock Generalization and network design strategies.
\newblock \emph{Connectionism in perspective}, 19:\penalty0 143--155, 1989.

\bibitem[Lenning et~al.(2018)Lenning, Fortunato, Le, Clark, Sherpa, Yi, Hofsteen, Thamilarasu, Yang, Xu, et~al.]{anom-bio}
Michael Lenning, Joseph Fortunato, Tai Le, Isaac Clark, Ang Sherpa, Soyeon Yi, Peter Hofsteen, Geethapriya Thamilarasu, Jingchun Yang, Xiaolei Xu, et~al.
\newblock Real-time monitoring and analysis of zebrafish electrocardiogram with anomaly detection.
\newblock \emph{Sensors}, 18\penalty0 (1):\penalty0 61, 2018.

\bibitem[Lerman and Maunu(2018{\natexlab{a}})]{lerman2018fast}
Gilad Lerman and Tyler Maunu.
\newblock Fast, robust and non-convex subspace recovery.
\newblock \emph{Information and Inference: A Journal of the IMA}, 7\penalty0 (2):\penalty0 277--336, 2018{\natexlab{a}}.

\bibitem[Lerman and Maunu(2018{\natexlab{b}})]{lerman2018overview}
Gilad Lerman and Tyler Maunu.
\newblock An overview of robust subspace recovery.
\newblock \emph{Proceedings of the IEEE}, 106\penalty0 (8):\penalty0 1380--1410, 2018{\natexlab{b}}.

\bibitem[Lerman et~al.(2015)Lerman, McCoy, Tropp, and Zhang]{lerman2015robust}
Gilad Lerman, Michael~B McCoy, Joel~A Tropp, and Teng Zhang.
\newblock Robust computation of linear models by convex relaxation.
\newblock \emph{Foundations of Computational Mathematics}, 15\penalty0 (2):\penalty0 363--410, 2015.

\bibitem[Li et~al.(2020)Li, Zhao, Botta, Ionescu, and Hu]{li2020copod}
Zheng Li, Yue Zhao, Nicola Botta, Cezar Ionescu, and Xiyang Hu.
\newblock Copod: copula-based outlier detection.
\newblock In \emph{2020 IEEE international conference on data mining (ICDM)}, pages 1118--1123. IEEE, 2020.

\bibitem[Li et~al.(2022)Li, Zhao, Hu, Botta, Ionescu, and Chen]{ecod}
Zheng Li, Yue Zhao, Xiyang Hu, Nicola Botta, Cezar Ionescu, and George Chen.
\newblock Ecod: Unsupervised outlier detection using empirical cumulative distribution functions.
\newblock \emph{IEEE Transactions on Knowledge and Data Engineering}, 2022.

\bibitem[Lin et~al.(2010)Lin, Chen, and Ma]{lin2010augmented}
Zhouchen Lin, Minming Chen, and Yi~Ma.
\newblock The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices.
\newblock \emph{arXiv preprint arXiv:1009.5055}, 2010.

\bibitem[Lindenbaum et~al.(2021{\natexlab{a}})Lindenbaum, Salhov, Averbuch, and Kluger]{lindenbaum2020deep}
Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, and Yuval Kluger.
\newblock L0-sparse canonical correlation analysis.
\newblock 2021{\natexlab{a}}.

\bibitem[Lindenbaum et~al.(2021{\natexlab{b}})Lindenbaum, Shaham, Svirsky, Peterfreund, and Kluger]{lindenbaum2020differentiable}
Ofir Lindenbaum, Uri Shaham, Jonathan Svirsky, Erez Peterfreund, and Yuval Kluger.
\newblock Differentiable unsupervised feature selection based on a gated laplacian.
\newblock \emph{Neural Information Processing Systems (NeurIPS)}, 2021{\natexlab{b}}.

\bibitem[Liu et~al.(2008)Liu, Ting, and Zhou]{Iforest}
Fei~Tony Liu, Kai~Ming Ting, and Zhi-Hua Zhou.
\newblock Isolation forest.
\newblock In \emph{2008 Eighth IEEE International Conference on Data Mining}, pages 413--422. IEEE, 2008.

\bibitem[Liu et~al.(2019)Liu, Li, Zhou, Jiang, Sun, Wang, and He]{liu2019generative}
Yezheng Liu, Zhe Li, Chong Zhou, Yuanchun Jiang, Jianshan Sun, Meng Wang, and Xiangnan He.
\newblock Generative adversarial active learning for unsupervised outlier detection.
\newblock \emph{IEEE Transactions on Knowledge and Data Engineering}, 32\penalty0 (8):\penalty0 1517--1528, 2019.

\bibitem[Maunu et~al.(2019)Maunu, Zhang, and Lerman]{maunu2019well}
Tyler Maunu, Teng Zhang, and Gilad Lerman.
\newblock A well-tempered landscape for non-convex robust subspace recovery.
\newblock \emph{Journal of Machine Learning Research}, 20\penalty0 (37), 2019.

\bibitem[Mitra et~al.(2010)Mitra, Veeraraghavan, and Chellappa]{mitra2010robust}
Kaushik Mitra, Ashok Veeraraghavan, and Rama Chellappa.
\newblock Robust regression using sparse learning for high dimensional parameter estimation problems.
\newblock In \emph{2010 IEEE International Conference on Acoustics, Speech and Signal Processing}, pages 3846--3849. IEEE, 2010.

\bibitem[Pearson(1901)]{Pearson1901PCA}
Karl Pearson.
\newblock L{I}{I}{I}. on lines and planes of closest fit to systems of points in space.
\newblock \emph{The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science}, 2\penalty0 (11):\penalty0 559--572, 1901.
\newblock \doi{10.1080/14786440109462720}.
\newblock URL \url{https://doi.org/10.1080/14786440109462720}.

\bibitem[Perera et~al.(2021)Perera, Oza, and Patel]{perera2021one}
Pramuditha Perera, Poojan Oza, and Vishal~M Patel.
\newblock One-class classification: A survey.
\newblock \emph{arXiv preprint arXiv:2101.03064}, 2021.

\bibitem[Peterfreund et~al.(2020)Peterfreund, Lindenbaum, Dietrich, Bertalan, Gavish, Kevrekidis, and Coifman]{peterfreund2020local}
Erez Peterfreund, Ofir Lindenbaum, Felix Dietrich, Tom Bertalan, Matan Gavish, Ioannis~G Kevrekidis, and Ronald~R Coifman.
\newblock Local conformal autoencoder for standardized data coordinates.
\newblock \emph{Proceedings of the National Academy of Sciences}, 117\penalty0 (49):\penalty0 30918--30927, 2020.

\bibitem[Qiu et~al.(2021)Qiu, Pfrommer, Kloft, Mandt, and Rudolph]{qiu2021neural}
Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, and Maja Rudolph.
\newblock Neural transformation learning for deep anomaly detection beyond images.
\newblock In \emph{International Conference on Machine Learning}, pages 8703--8714. PMLR, 2021.

\bibitem[Ramaswamy et~al.(2000)Ramaswamy, Rastogi, and Shim]{ramaswamy2000efficient}
Sridhar Ramaswamy, Rajeev Rastogi, and Kyuseok Shim.
\newblock Efficient algorithms for mining outliers from large data sets.
\newblock In \emph{Proceedings of the 2000 ACM SIGMOD international conference on Management of data}, pages 427--438, 2000.

\bibitem[Reiss et~al.(2021)Reiss, Cohen, Bergman, and Hoshen]{reiss2021panda}
Tal Reiss, Niv Cohen, Liron Bergman, and Yedid Hoshen.
\newblock Panda: Adapting pretrained features for anomaly detection and segmentation.
\newblock In \emph{Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages 2806--2814, 2021.

\bibitem[Ronen et~al.(2019)Ronen, Jacobs, Kasten, and Kritchman]{ronen2019convergence}
Basri Ronen, David Jacobs, Yoni Kasten, and Shira Kritchman.
\newblock The convergence rate of neural networks for learned functions of different frequencies.
\newblock \emph{Advances in Neural Information Processing Systems}, 32, 2019.

\bibitem[Roweis and Saul(2000)]{roweis2000LLE}
Sam~T Roweis and Lawrence~K Saul.
\newblock Nonlinear dimensionality reduction by locally linear embedding.
\newblock \emph{Science}, 290\penalty0 (5500):\penalty0 2323--2326, 2000.

\bibitem[Rozner et~al.(2024)Rozner, Battash, Li, Wolf, and Lindenbaum]{rozner2023anomaly}
Amit Rozner, Barak Battash, Henry Li, Lior Wolf, and Ofir Lindenbaum.
\newblock Anomaly detection with variance stabilized density estimation.
\newblock \emph{The Conference on Uncertainty in Artificial Intelligence (UAI)}, 2024.

\bibitem[Ruff et~al.(2018)Ruff, Vandermeulen, Goernitz, Deecke, Siddiqui, Binder, M{\"u}ller, and Kloft]{ruff2018deep}
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib~Ahmed Siddiqui, Alexander Binder, Emmanuel M{\"u}ller, and Marius Kloft.
\newblock Deep one-class classification.
\newblock In \emph{International conference on machine learning}, pages 4393--4402. PMLR, 2018.

\bibitem[Rumelhart et~al.(1985)Rumelhart, Hinton, and Williams]{rumelhart1985learning}
David~E Rumelhart, Geoffrey~E Hinton, and Ronald~J Williams.
\newblock Learning internal representations by error propagation.
\newblock Technical report, California Univ San Diego La Jolla Inst for Cognitive Science, 1985.

\bibitem[Sakurada and Yairi(2014)]{sakurada2014anomaly}
Mayu Sakurada and Takehisa Yairi.
\newblock Anomaly detection using autoencoders with nonlinear dimensionality reduction.
\newblock In \emph{Proceedings of the MLSDA 2014 2nd workshop on machine learning for sensory data analysis}, pages 4--11, 2014.

\bibitem[Sch{\"o}lkopf et~al.(2001)Sch{\"o}lkopf, Platt, Shawe-Taylor, Smola, and Williamson]{scholkopf2001estimating}
Bernhard Sch{\"o}lkopf, John~C Platt, John Shawe-Taylor, Alex~J Smola, and Robert~C Williamson.
\newblock Estimating the support of a high-dimensional distribution.
\newblock \emph{Neural computation}, 13\penalty0 (7):\penalty0 1443--1471, 2001.

\bibitem[Shenkar and Wolf(2022)]{shenkar2022anomaly}
Tom Shenkar and Lior Wolf.
\newblock Anomaly detection for tabular data with internal contrastive learning.
\newblock In \emph{International Conference on Learning Representations}, 2022.

\bibitem[Tang et~al.(2002{\natexlab{a}})Tang, Chen, Fu, and Cheung]{COF}
Jian Tang, Zhixiang Chen, Ada Wai-Chee Fu, and David~W Cheung.
\newblock Enhancing effectiveness of outlier detections for low density patterns.
\newblock In \emph{Pacific-Asia Conference on Knowledge Discovery and Data Mining}, pages 535--548. Springer, 2002{\natexlab{a}}.

\bibitem[Tang et~al.(2002{\natexlab{b}})Tang, Chen, Fu, and Cheung]{tang2002enhancing}
Jian Tang, Zhixiang Chen, Ada Wai-Chee Fu, and David~W Cheung.
\newblock Enhancing effectiveness of outlier detections for low density patterns.
\newblock In \emph{Pacific-Asia Conference on Knowledge Discovery and Data Mining}, pages 535--548. Springer, 2002{\natexlab{b}}.

\bibitem[Tishby and Zaslavsky(2015)]{tishby2015deep}
Naftali Tishby and Noga Zaslavsky.
\newblock Deep learning and the information bottleneck principle.
\newblock In \emph{2015 ieee information theory workshop (itw)}, pages 1--5. IEEE, 2015.

\bibitem[Wang et~al.(2019)Wang, Zeng, Liu, Zhu, Yin, Xu, and Kloft]{wang2019effective}
Siqi Wang, Yijie Zeng, Xinwang Liu, En~Zhu, Jianping Yin, Chuanfu Xu, and Marius Kloft.
\newblock Effective end-to-end unsupervised outlier detection via inlier priority of discriminative network.
\newblock In \emph{NeurIPS}, pages 5960--5973, 2019.

\bibitem[Yamada et~al.(2020)Yamada, Lindenbaum, Negahban, and Kluger]{yamada2018feature}
Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, and Yuval Kluger.
\newblock Feature selection using stochastic gates.
\newblock In \emph{International Conference on Machine Learning}, pages 10648--10659. PMLR, 2020.

\bibitem[Yang et~al.(2018)Yang, Lozano, and Aravkin]{yangrobust}
Eunho Yang, Aur{\'e}lie~C Lozano, and Aleksandr Aravkin.
\newblock A general family of trimmed estimators for robust high-dimensional data analysis.
\newblock 2018.

\bibitem[Zhang(2016)]{zhang2016robust}
Teng Zhang.
\newblock Robust subspace recovery by tyler's m-estimator.
\newblock \emph{Information and Inference: A Journal of the IMA}, 5\penalty0 (1):\penalty0 1--21, 2016.

\bibitem[Zhao et~al.(2019{\natexlab{a}})Zhao, Nasrullah, Hryniewicki, and Li]{lscp}
Yue Zhao, Zain Nasrullah, Maciej~K Hryniewicki, and Zheng Li.
\newblock Lscp: Locally selective combination in parallel outlier ensembles.
\newblock In \emph{Proceedings of the 2019 SIAM International Conference on Data Mining}, pages 585--593. SIAM, 2019{\natexlab{a}}.

\bibitem[Zhao et~al.(2019{\natexlab{b}})Zhao, Nasrullah, and Li]{zhao2019pyod}
Yue Zhao, Zain Nasrullah, and Zheng Li.
\newblock Pyod: A python toolbox for scalable outlier detection.
\newblock \emph{Journal of Machine Learning Research}, 20\penalty0 (96):\penalty0 1--7, 2019{\natexlab{b}}.
\newblock URL \url{http://jmlr.org/papers/v20/19-011.html}.

\bibitem[Zheng et~al.(2017)Zheng, Terry, Belgrader, Ryvkin, Bent, Wilson, Ziraldo, Wheeler, McDermott, Zhu, et~al.]{zheng2017massively}
Grace~XY Zheng, Jessica~M Terry, Phillip Belgrader, Paul Ryvkin, Zachary~W Bent, Ryan Wilson, Solongo~B Ziraldo, Tobias~D Wheeler, Geoff~P McDermott, Junjie Zhu, et~al.
\newblock Massively parallel digital transcriptional profiling of single cells.
\newblock \emph{Nature communications}, 8\penalty0 (1):\penalty0 1--12, 2017.

\bibitem[Zhou and Paffenroth(2017)]{zhou2017anomaly}
Chong Zhou and Randy~C Paffenroth.
\newblock Anomaly detection with robust deep autoencoders.
\newblock In \emph{Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, pages 665--674, 2017.

\bibitem[Zong et~al.(2018)Zong, Song, Min, Cheng, Lumezanu, Cho, and Chen]{zong2018deep}
Bo~Zong, Qi~Song, Martin~Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen.
\newblock Deep autoencoding gaussian mixture model for unsupervised anomaly detection.
\newblock 2018.

\end{thebibliography}
