@article{von2007tutorial,
  title={A tutorial on spectral clustering},
  author={Von Luxburg, Ulrike},
  journal={Statistics and computing},
  volume={17},
  number={4},
  pages={395--416},
  year={2007},
  publisher={Springer}
}
@inproceedings{li2020copod,
  title={COPOD: copula-based outlier detection},
  author={Li, Zheng and Zhao, Yue and Botta, Nicola and Ionescu, Cezar and Hu, Xiyang},
  booktitle={2020 IEEE international conference on data mining (ICDM)},
  pages={1118--1123},
  year={2020},
  organization={IEEE}
}
@inproceedings{shenkar2022anomaly,
  title={Anomaly detection for tabular data with internal contrastive learning},
  author={Shenkar, Tom and Wolf, Lior},
  booktitle={International Conference on Learning Representations},
  year={2022}
}
@article{dolan2002benchmarking,
  title={Benchmarking optimization software with performance profiles},
  author={Dolan, Elizabeth D and Mor{\'e}, Jorge J},
  journal={Mathematical programming},
  volume={91},
  pages={201--213},
  year={2002},
  publisher={Springer}
}
@inproceedings{mitra2010robust,
  title={Robust regression using sparse learning for high dimensional parameter estimation problems},
  author={Mitra, Kaushik and Veeraraghavan, Ashok and Chellappa, Rama},
  booktitle={2010 IEEE International Conference on Acoustics, Speech and Signal Processing},
  pages={3846--3849},
  year={2010},
  organization={IEEE}
}
@article{yangrobust,
  title={A general family of trimmed estimators for robust high-dimensional data analysis},
  author={Yang, Eunho and Lozano, Aur{\'e}lie C and Aravkin, Aleksandr},
  year={2018}
}
@article{huber1987place,
  title={The place of the L1-norm in robust estimation},
  author={Huber, Peter J},
  journal={Computational Statistics \& Data Analysis},
  volume={5},
  number={4},
  pages={255--262},
  year={1987},
  publisher={Elsevier}
}
@article{ecod,
  title={Ecod: Unsupervised outlier detection using empirical cumulative distribution functions},
  author={Li, Zheng and Zhao, Yue and Hu, Xiyang and Botta, Nicola and Ionescu, Cezar and Chen, George},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2022},
  publisher={IEEE}
}
@inproceedings{Iforest,
  title={Isolation forest},
  author={Liu, Fei Tony and Ting, Kai Ming and Zhou, Zhi-Hua},
  booktitle={2008 Eighth IEEE International Conference on Data Mining},
  pages={413--422},
  year={2008},
  organization={IEEE}
}
@inproceedings{qiu2021neural,
  title={Neural transformation learning for deep anomaly detection beyond images},
  author={Qiu, Chen and Pfrommer, Timo and Kloft, Marius and Mandt, Stephan and Rudolph, Maja},
  booktitle={International Conference on Machine Learning},
  pages={8703--8714},
  year={2021},
  organization={PMLR}
}

@inproceedings{angiulli2002fast5555,
  title={Fast outlier detection in high dimensional spaces},
  author={Angiulli, Fabrizio and Pizzuti, Clara},
  booktitle={European conference on principles of data mining and knowledge discovery},
  pages={15--27},
  year={2002},
  organization={Springer}
}
@article{zheng2017massively,
  title={Massively parallel digital transcriptional profiling of single cells},
  author={Zheng, Grace XY and Terry, Jessica M and Belgrader, Phillip and Ryvkin, Paul and Bent, Zachary W and Wilson, Ryan and Ziraldo, Solongo B and Wheeler, Tobias D and McDermott, Geoff P and Zhu, Junjie and others},
  journal={Nature communications},
  volume={8},
  number={1},
  pages={1--12},
  year={2017},
  publisher={Nature Publishing Group}
}
@article{ng2011sparse,
  title={Sparse autoencoder},
  author={Ng, Andrew and others},
  journal={CS294A Lecture notes},
  volume={72},
  number={2011},
  pages={1--19},
  year={2011}
}
@inproceedings{goyal2020drocc,
  title={DROCC: Deep robust one-class classification},
  author={Goyal, Sachin and Raghunathan, Aditi and Jain, Moksh and Simhadri, Harsha Vardhan and Jain, Prateek},
  booktitle={International Conference on Machine Learning},
  pages={3711--3721},
  year={2020},
  organization={PMLR}
}

@inproceedings{reiss2021panda,
  title={Panda: Adapting pretrained features for anomaly detection and segmentation},
  author={Reiss, Tal and Cohen, Niv and Bergman, Liron and Hoshen, Yedid},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2806--2814},
  year={2021}
}
@article{du2019implicit,
  title={Implicit generation and modeling with energy based models},
  author={Du, Yilun and Mordatch, Igor},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  year={2019}
}
@article{HBOS,
  title={Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm},
  author={Goldstein, Markus and Dengel, Andreas},
  journal={KI-2012: Poster and Demo Track},
  pages={59--63},
  year={2012},
  publisher={Citeseer}
}
@inproceedings{yoon2021autoencoding,
  title={Autoencoding under normalization constraints},
  author={Yoon, Sangwoong and Noh, Yung-Kyun and Park, Frank},
  booktitle={International Conference on Machine Learning},
  pages={12087--12097},
  year={2021},
  organization={PMLR}
}
@inproceedings{lscp,
  title={LSCP: Locally selective combination in parallel outlier ensembles},
  author={Zhao, Yue and Nasrullah, Zain and Hryniewicki, Maciej K and Li, Zheng},
  booktitle={Proceedings of the 2019 SIAM International Conference on Data Mining},
  pages={585--593},
  year={2019},
  organization={SIAM}
}

@inproceedings{SOD,
  title={Outlier detection in axis-parallel subspaces of high dimensional data},
  author={Kriegel, Hans-Peter and Kr{\"o}ger, Peer and Schubert, Erich and Zimek, Arthur},
  booktitle={Pacific-Asia Conference on Knowledge Discovery and Data Mining},
  pages={831--838},
  year={2009},
  organization={Springer}
}
@inproceedings{cof,
  title={Enhancing effectiveness of outlier detections for low density patterns},
  author={Tang, Jian and Chen, Zhixiang and Fu, Ada Wai-Chee and Cheung, David W},
  booktitle={Pacific-Asia Conference on Knowledge Discovery and Data Mining},
  pages={535--548},
  year={2002},
  organization={Springer}
}

@inproceedings{abod,
  title={Angle-based outlier detection in high-dimensional data},
  author={Kriegel, Hans-Peter and Schubert, Matthias and Zimek, Arthur},
  booktitle={Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining},
  pages={444--452},
  year={2008}
}

@article{cblof,
  title={Discovering cluster-based local outliers},
  author={He, Zengyou and Xu, Xiaofei and Deng, Shengchun},
  journal={Pattern Recognition Letters},
  volume={24},
  number={9-10},
  pages={1641--1650},
  year={2003},
  publisher={Elsevier}
}
@inproceedings{sakurada2014anomaly,
  title={Anomaly detection using autoencoders with nonlinear dimensionality reduction},
  author={Sakurada, Mayu and Yairi, Takehisa},
  booktitle={Proceedings of the MLSDA 2014 2nd workshop on machine learning for sensory data analysis},
  pages={4--11},
  year={2014}
}
@book{boyd2004convex,
  title={Convex optimization},
  author={Boyd, Stephen and Boyd, Stephen P and Vandenberghe, Lieven},
  year={2004},
  publisher={Cambridge university press}
}

@inproceedings{chen2017outlier,
  title={Outlier detection with autoencoder ensembles},
  author={Chen, Jinghui and Sathe, Saket and Aggarwal, Charu and Turaga, Deepak},
  booktitle={Proceedings of the 2017 SIAM international conference on data mining},
  pages={90--98},
  year={2017},
  organization={SIAM}
}

@article{ishii2022learning,
  title={Learning low-dimensional manifolds under the L0-norm constraint for unsupervised outlier detection},
  author={Ishii, Yoshinao and Koide, Satoshi and Hayakawa, Keiichiro},
  journal={International Journal of Data Science and Analytics},
  pages={1--15},
  year={2022},
  publisher={Springer}
}
@article{ishii2019low,
  title={Low-cost unsupervised outlier detection by autoencoders with robust estimation},
  author={Ishii, Yoshinao and Takanashi, Masaki},
  journal={Journal of Information Processing},
  volume={27},
  pages={335--339},
  year={2019},
  publisher={Information Processing Society of Japan}
}

@article{zong2018deep,
  title={Deep autoencoding gaussian mixture model for unsupervised anomaly detection},
  author={Zong, Bo and Song, Qi and Min, Martin Renqiang and Cheng, Wei and Lumezanu, Cristian and Cho, Daeki and Chen, Haifeng},
  year={2018}
}

@inproceedings{zhou2017anomaly,
  title={Anomaly detection with robust deep autoencoders},
  author={Zhou, Chong and Paffenroth, Randy C},
  booktitle={Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  pages={665--674},
  year={2017}
}

@misc{Rayana:2016,
author = "Shebuti, Rayana",
year = "2016",
title = "{ODDS} Library",
url = "http://odds.cs.stonybrook.edu",
institution = "Stony Brook University, Department of Computer Sciences" }


@misc{Dua:2019 ,
author = "Dua, Dheeru and Graff, Casey",
year = "2017",
title = "{UCI} Machine Learning Repository",
url = "http://archive.ics.uci.edu/ml",
institution = "University of California, Irvine, School of Information and Computer Sciences" }

@inproceedings{chen2001one,
  title={One-class SVM for learning in image retrieval},
  author={Chen, Yunqiang and Zhou, Xiang Sean and Huang, Thomas S},
  booktitle={Proceedings 2001 International Conference on Image Processing (Cat. No. 01CH37205)},
  volume={1},
  pages={34--37},
  year={2001},
  organization={IEEE}
}

@inproceedings{zhai2016deep,
  title={Deep Structured Energy Based Models for Anomaly Detection},
  author={Zhai, Shuangfei and Cheng, Yu and Lu, Weining and Zhang, Zhongfei},
  booktitle={International Conference on Machine Learning},
  pages={1100--1109},
  year={2016}
}

@inproceedings{yang2017towards,
  title={Towards k-means-friendly spaces: Simultaneous deep learning and clustering},
  author={Yang, Bo and Fu, Xiao and Sidiropoulos, Nicholas D and Hong, Mingyi},
  booktitle={Proceedings of the 34th International Conference on Machine Learning-Volume 70},
  pages={3861--3870},
  year={2017},
  organization={JMLR. org}
}


@inproceedings{he2004locality,
  title={Locality preserving projections},
  author={He, Xiaofei and Niyogi, Partha},
  booktitle={Advances in neural information processing systems},
  pages={153--160},
  year={2004}
}

@article{maddison2016concrete,
  title={The concrete distribution: A continuous relaxation of discrete random variables},
  author={Maddison, Chris J and Mnih, Andriy and Teh, Yee Whye},
  journal={arXiv preprint arXiv:1611.00712},
  year={2016}
}


@inproceedings{Gumbel1,
title	= {Categorical Reparameterization with Gumbel-Softmax},
author	= {Eric Jang and Shixiang Gu and Ben Poole},
year	= {2017},
URL	= {https://arxiv.org/abs/1611.01144}
}

@article{jang2016categorical,
  title={Categorical reparameterization with gumbel-softmax},
  author={Jang, Eric and Gu, Shixiang and Poole, Ben},
  journal={arXiv preprint arXiv:1611.01144},
  year={2016}
}


@inproceedings{reparameterization2,
  title={Implicit reparameterization gradients},
  author={Figurnov, Mikhail and Mohamed, Shakir and Mnih, Andriy},
  booktitle={Advances in Neural Information Processing Systems},
  pages={441--452},
  year={2018}
}
@inproceedings{reparameterization1,
  title={Reducing reparameterization gradient variance},
  author={Miller, Andrew and Foti, Nick and D'Amour, Alexander and Adams, Ryan P},
  booktitle={Advances in Neural Information Processing Systems},
  pages={3708--3718},
  year={2017}
}

@inproceedings{yamada2018feature,
  title={Feature selection using stochastic gates},
  author={Yamada, Yutaro and Lindenbaum, Ofir and Negahban, Sahand and Kluger, Yuval},
  booktitle={International Conference on Machine Learning},
  pages={10648--10659},
  year={2020},
  organization={PMLR}
}

@article{louizos2017learning,
  title={Learning Sparse Neural Networks through $ L\_0 $ Regularization},
  author={Louizos, Christos and Welling, Max and Kingma, Diederik P},
  journal={arXiv preprint arXiv:1712.01312},
  year={2017}
}

@article{lerman2018overview,
  title={An overview of robust subspace recovery},
  author={Lerman, Gilad and Maunu, Tyler},
  journal={Proceedings of the IEEE},
  volume={106},
  number={8},
  pages={1380--1410},
  year={2018},
  publisher={IEEE}
}

@inproceedings{breunig2000lof,
  title={LOF: identifying density-based local outliers},
  author={Breunig, Markus M and Kriegel, Hans-Peter and Ng, Raymond T and Sander, J{\"o}rg},
  booktitle={Proceedings of the 2000 ACM SIGMOD international conference on Management of data},
  pages={93--104},
  year={2000}
}

@inproceedings{jin2001mining,
  title={Mining top-n local outliers in large databases},
  author={Jin, Wen and Tung, Anthony KH and Han, Jiawei},
  booktitle={Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining},
  pages={293--298},
  year={2001}
}

@inproceedings{tang2002enhancing,
  title={Enhancing effectiveness of outlier detections for low density patterns},
  author={Tang, Jian and Chen, Zhixiang and Fu, Ada Wai-Chee and Cheung, David W},
  booktitle={Pacific-Asia Conference on Knowledge Discovery and Data Mining},
  pages={535--548},
  year={2002},
  organization={Springer}
}

@inproceedings{jin2006ranking,
  title={Ranking outliers using symmetric neighborhood relationship},
  author={Jin, Wen and Tung, Anthony KH and Han, Jiawei and Wang, Wei},
  booktitle={Pacific-Asia conference on knowledge discovery and data mining},
  pages={577--593},
  year={2006},
  organization={Springer}
}

@inproceedings{kriegel2009loop,
  title={LoOP: local outlier probabilities},
  author={Kriegel, Hans-Peter and Kr{\"o}ger, Peer and Schubert, Erich and Zimek, Arthur},
  booktitle={Proceedings of the 18th ACM conference on Information and knowledge management},
  pages={1649--1652},
  year={2009}
}

@article{constantinou2018pynomaly,
  title={PyNomaly: Anomaly detection using Local Outlier Probabilities (LoOP).},
  author={Constantinou, Valentino},
  journal={Journal of Open Source Software},
  volume={3},
  number={30},
  pages={845},
  year={2018}
}

@inproceedings{ramaswamy2000efficient,
  title={Efficient algorithms for mining outliers from large data sets},
  author={Ramaswamy, Sridhar and Rastogi, Rajeev and Shim, Kyuseok},
  booktitle={Proceedings of the 2000 ACM SIGMOD international conference on Management of data},
  pages={427--438},
  year={2000}
}

@inproceedings{angiulli2002fast,
  title={Fast outlier detection in high dimensional spaces},
  author={Angiulli, Fabrizio and Pizzuti, Clara},
  booktitle={European conference on principles of data mining and knowledge discovery},
  pages={15--27},
  year={2002},
  organization={Springer}
}

@article{ghoting2008fast,
  title={Fast mining of distance-based outliers in high-dimensional datasets},
  author={Ghoting, Amol and Parthasarathy, Srinivasan and Otey, Matthew Eric},
  journal={Data Mining and Knowledge Discovery},
  volume={16},
  number={3},
  pages={349--364},
  year={2008},
  publisher={Springer}
}

@article{liu2019generative,
  title={Generative adversarial active learning for unsupervised outlier detection},
  author={Liu, Yezheng and Li, Zhe and Zhou, Chong and Jiang, Yuanchun and Sun, Jianshan and Wang, Meng and He, Xiangnan},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  volume={32},
  number={8},
  pages={1517--1528},
  year={2019},
  publisher={IEEE}
}

@article{hendrycks2018deep,
  title={Deep anomaly detection with outlier exposure},
  author={Hendrycks, Dan and Mazeika, Mantas and Dietterich, Thomas},
  journal={arXiv preprint arXiv:1812.04606},
  year={2018}
}

@inproceedings{wang2019effective,
  title={Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network.},
  author={Wang, Siqi and Zeng, Yijie and Liu, Xinwang and Zhu, En and Yin, Jianping and Xu, Chuanfu and Kloft, Marius},
  booktitle={NeurIPS},
  pages={5960--5973},
  year={2019}
}

@article{abdi2010principal,
  title={Principal component analysis},
  author={Abdi, Herv{\'e} and Williams, Lynne J},
  journal={Wiley interdisciplinary reviews: computational statistics},
  volume={2},
  number={4},
  pages={433--459},
  year={2010},
  publisher={Wiley Online Library}
}

@article{lecun1989generalization,
  title={Generalization and network design strategies},
  author={LeCun, Yann and others},
  journal={Connectionism in perspective},
  volume={19},
  pages={143--155},
  year={1989},
  publisher={Elsevier Zurich, Switzerland}
}

@article{sparseNN-group-lasso,
 author = {Scardapane, Simone and Comminiello, Danilo and Hussain, Amir and Uncini, Aurelio},
 title = {Group Sparse Regularization for Deep Neural Networks},
 journal = {Neurocomput.},
 issue_date = {June 2017},
 volume = {241},
 number = {C},
 month = jun,
 year = {2017},
 issn = {0925-2312},
 pages = {81--89},
 numpages = {9},
 url = {https://doi.org/10.1016/j.neucom.2017.02.029},
 doi = {10.1016/j.neucom.2017.02.029},
 acmid = {3067328},
 publisher = {Elsevier Science Publishers B. V.},
 address = {Amsterdam, The Netherlands, The Netherlands},
 keywords = {Deep networks, Feature selection, Group sparsity, Pruning},
} 
@article{plaut2018principal,
  title={From principal subspaces to principal components with linear autoencoders},
  author={Plaut, Elad},
  journal={arXiv preprint arXiv:1804.10253},
  year={2018}
}
@article{lindenbaum2020deep,
  title={L0-sparse canonical correlation analysis},
  author={Lindenbaum, Ofir and Salhov, Moshe and Averbuch, Amir and Kluger, Yuval},
  booktitle={International Conference on Learning Representations},
  year={2021}
}

@article{rozner2023anomaly,
  title={Anomaly Detection with Variance Stabilized Density Estimation},
  author={Rozner, Amit and Battash, Barak and Li, Henry and Wolf, Lior and Lindenbaum, Ofir},
  journal={The Conference on Uncertainty in Artificial Intelligence (UAI)},
  year={2024}
}
@article{lindenbaum2020differentiable,
  title={Differentiable Unsupervised Feature Selection based on a Gated Laplacian},
  author={Lindenbaum, Ofir and Shaham, Uri and Svirsky, Jonathan and Peterfreund, Erez and Kluger, Yuval},
  journal={Neural Information Processing Systems (NeurIPS)},
  year={2021}
}
@article{lindenbaum2020ell,
  title={$ell\_0 $-based Sparse Canonical Correlation Analysis},
  author={Lindenbaum, Ofir and Salhov, Moshe and Averbuch, Amir and Kluger, Yuval},
  journal={arXiv preprint arXiv:2010.05620},
  year={2020}
}
@article{jana2021support,
  title={Support Recovery with Stochastic Gates: Theory and Application for Linear Models},
  author={Jana, Soham and Li, Henry and Yamada, Yutaro and Lindenbaum, Ofir},
  journal={arXiv preprint arXiv:2110.15960},
  year={2021}
}
@inproceedings{lindenbaum2016multi,
  title={Multi-channel fusion for seismic event detection and classification},
  author={Lindenbaum, Ofir and Rabin, Neta and Bregman, Yuri and Averbuch, Amir},
  booktitle={2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE)},
  pages={1--5},
  year={2016},
  organization={IEEE}
}
@article{bregman2021array,
  title={Array based earthquakes-explosion discrimination using diffusion maps},
  author={Bregman, Y and Lindenbaum, O and Rabin, N},
  journal={Pure and Applied Geophysics},
  volume={178},
  number={7},
  pages={2403--2418},
  year={2021},
  publisher={Springer}
}
@article{anom-cyber,
  title={Bidirectional LSTM autoencoder for sequence based anomaly detection in cyber security.},
  author={Chawla, Ashima and Jacob, Paul and Lee, Brian and Fallon, Sheila},
  journal={International Journal of Simulation--Systems, Science \& Technology},
  year={2019},
  publisher={United Kingdom Simulation Society}
}
@article{anom-bio,
  title={Real-time monitoring and analysis of zebrafish electrocardiogram with anomaly detection},
  author={Lenning, Michael and Fortunato, Joseph and Le, Tai and Clark, Isaac and Sherpa, Ang and Yi, Soyeon and Hofsteen, Peter and Thamilarasu, Geethapriya and Yang, Jingchun and Xu, Xiaolei and others},
  journal={Sensors},
  volume={18},
  number={1},
  pages={61},
  year={2018},
  publisher={Multidisciplinary Digital Publishing Institute}
}
@article{peterfreund2020local,
  title={Local conformal autoencoder for standardized data coordinates},
  author={Peterfreund, Erez and Lindenbaum, Ofir and Dietrich, Felix and Bertalan, Tom and Gavish, Matan and Kevrekidis, Ioannis G and Coifman, Ronald R},
  journal={Proceedings of the National Academy of Sciences},
  volume={117},
  number={49},
  pages={30918--30927},
  year={2020},
  publisher={National Acad Sciences}
}
@article{scholkopf2001estimating,
  title={Estimating the support of a high-dimensional distribution},
  author={Sch{\"o}lkopf, Bernhard and Platt, John C and Shawe-Taylor, John and Smola, Alex J and Williamson, Robert C},
  journal={Neural computation},
  volume={13},
  number={7},
  pages={1443--1471},
  year={2001},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}

@inproceedings{eduardo2020robust,
  title={Robust variational autoencoders for outlier detection and repair of mixed-type data},
  author={Eduardo, Simao and Naz{\'a}bal, Alfredo and Williams, Christopher KI and Sutton, Charles},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={4056--4066},
  year={2020},
  organization={PMLR}
}
@article{lai2019robust,
  title={Robust subspace recovery layer for unsupervised anomaly detection},
  author={Lai, Chieh-Hsin and Zou, Dongmian and Lerman, Gilad},
  journal={arXiv preprint arXiv:1904.00152},
  year={2019}
}
@article{hendrycks2019using,
  title={Using self-supervised learning can improve model robustness and uncertainty},
  author={Hendrycks, Dan and Mazeika, Mantas and Kadavath, Saurav and Song, Dawn},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  year={2019}
}

@inproceedings{deecke2021transfer,
  title={Transfer-based semantic anomaly detection},
  author={Deecke, Lucas and Ruff, Lukas and Vandermeulen, Robert A and Bilen, Hakan},
  booktitle={International Conference on Machine Learning},
  pages={2546--2558},
  year={2021},
  organization={PMLR}
}

@article{bergman2020classification,
  title={Classification-based anomaly detection for general data},
  author={Bergman, Liron and Hoshen, Yedid},
  journal={arXiv preprint arXiv:2005.02359},
  year={2020}
}
@inproceedings{ruff2018deep,
  title={Deep one-class classification},
  author={Ruff, Lukas and Vandermeulen, Robert and Goernitz, Nico and Deecke, Lucas and Siddiqui, Shoaib Ahmed and Binder, Alexander and M{\"u}ller, Emmanuel and Kloft, Marius},
  booktitle={International conference on machine learning},
  pages={4393--4402},
  year={2018},
  organization={PMLR}
}

@article{perera2021one,
  title={One-class classification: A survey},
  author={Perera, Pramuditha and Oza, Poojan and Patel, Vishal M},
  journal={arXiv preprint arXiv:2101.03064},
  year={2021}
}
@ARTICLE{one-layer,
   author = {{Feng}, J. and {Simon}, N.},
    title = "{Sparse-Input Neural Networks for High-dimensional Nonparametric Regression and Classification}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1711.07592},
 primaryClass = "stat.ME",
 keywords = {Statistics - Methodology, Statistics - Machine Learning},
     year = 2017,
    month = nov,
   adsurl = {http://adsabs.harvard.edu/abs/2017arXiv171107592F},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{DFS,
  title={Deep feature selection: theory and application to identify enhancers and promoters},
  author={Li, Yifeng and Chen, Chih-Yu and Wasserman, Wyeth W},
  journal={Journal of Computational Biology},
  volume={23},
  number={5},
  pages={322--336},
  year={2016},
  publisher={Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA}
}

@article{Pearson1901PCA,
author = { Karl   Pearson },
title = {L{I}{I}{I}. On lines and planes of closest fit to systems of points in space},
journal = {The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science},
volume = {2},
number = {11},
pages = {559-572},
year  = {1901},
publisher = {Taylor & Francis},
doi = {10.1080/14786440109462720},
URL = {https://doi.org/10.1080/14786440109462720},
eprint = {https://doi.org/10.1080/14786440109462720}
}

@article{aizenbud2021non,
  title={Non-Parametric Estimation of Manifolds from Noisy Data},
  author={Aizenbud, Yariv and Sober, Barak},
  journal={arXiv preprint arXiv:2105.04754},
  year={2021}
}
@article{roweis2000LLE,
  title={Nonlinear dimensionality reduction by locally linear embedding},
  author={Roweis, Sam T and Saul, Lawrence K},
  journal={Science},
  volume={290},
  number={5500},
  pages={2323--2326},
  year={2000},
  publisher={American Association for the Advancement of Science}
}

@inproceedings{tsakiris2015dual,
  title={Dual principal component pursuit},
  author={Tsakiris, Manolis C and Vidal, Ren{\'e}},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision Workshops},
  pages={10--18},
  year={2015}
}
@article{lerman2018fast,
  title={Fast, robust and non-convex subspace recovery},
  author={Lerman, Gilad and Maunu, Tyler},
  journal={Information and Inference: A Journal of the IMA},
  volume={7},
  number={2},
  pages={277--336},
  year={2018},
  publisher={Oxford University Press}
}

@inproceedings{zhang2009median,
  title={Median k-flats for hybrid linear modeling with many outliers},
  author={Zhang, Teng and Szlam, Arthur and Lerman, Gilad},
  booktitle={2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops},
  pages={234--241},
  year={2009},
  organization={IEEE}
}

@article{lerman2015robust,
  title={Robust computation of linear models by convex relaxation},
  author={Lerman, Gilad and McCoy, Michael B and Tropp, Joel A and Zhang, Teng},
  journal={Foundations of Computational Mathematics},
  volume={15},
  number={2},
  pages={363--410},
  year={2015},
  publisher={Springer}
}

@inproceedings{hardt2013algorithms,
  title={Algorithms and hardness for robust subspace recovery},
  author={Hardt, Moritz and Moitra, Ankur},
  booktitle={Conference on Learning Theory},
  pages={354--375},
  year={2013},
  organization={PMLR}
}

@article{zhang2016robust,
  title={Robust subspace recovery by Tyler's M-estimator},
  author={Zhang, Teng},
  journal={Information and Inference: A Journal of the IMA},
  volume={5},
  number={1},
  pages={1--21},
  year={2016},
  publisher={Oxford University Press}
}
@article{lin2010augmented,
  title={The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices},
  author={Lin, Zhouchen and Chen, Minming and Ma, Yi},
  journal={arXiv preprint arXiv:1009.5055},
  year={2010}
}

@article{aizenbud2015pca,
  title={PCA-based out-of-sample extension for dimensionality reduction},
  author={Aizenbud, Yariv and Bermanis, Amit and Averbuch, Amir},
  journal={arXiv preprint arXiv:1511.00831},
  year={2015}
}

@article{maunu2019well,
  title={A well-tempered landscape for non-convex robust subspace recovery},
  author={Maunu, Tyler and Zhang, Teng and Lerman, Gilad},
  journal={Journal of Machine Learning Research},
  volume={20},
  number={37},
  year={2019}
}
@article{zhao2019pyod,
  author  = {Zhao, Yue and Nasrullah, Zain and Li, Zheng},
  title   = {PyOD: A Python Toolbox for Scalable Outlier Detection},
  journal = {Journal of Machine Learning Research},
  year    = {2019},
  volume  = {20},
  number  = {96},
  pages   = {1-7},
  url     = {http://jmlr.org/papers/v20/19-011.html}
}

@inproceedings{deng2022anomaly,
  title={Anomaly Detection via Reverse Distillation from One-Class Embedding},
  author={Deng, Hanqiu and Li, Xingyu},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9737--9746},
  year={2022}
}

@article{hojjati2021dasvdd,
  title={Dasvdd: Deep autoencoding support vector data descriptor for anomaly detection},
  author={Hojjati, Hadi and Armanfard, Narges},
  journal={arXiv preprint arXiv:2106.05410},
  year={2021}
}

@inproceedings{ishii2020l0,
  title={L0-norm constrained autoencoders for unsupervised outlier detection},
  author={Ishii, Yoshinao and Koide, Satoshi and Hayakawa, Keiichiro},
  booktitle={Pacific-Asia Conference on Knowledge Discovery and Data Mining},
  pages={674--687},
  year={2020},
  organization={Springer}
}

@inproceedings{tishby2015deep,
  title={Deep learning and the information bottleneck principle},
  author={Tishby, Naftali and Zaslavsky, Noga},
  booktitle={2015 ieee information theory workshop (itw)},
  pages={1--5},
  year={2015},
  organization={IEEE}
}

@article{ronen2019convergence,
  title={The convergence rate of neural networks for learned functions of different frequencies},
  author={Ronen, Basri and Jacobs, David and Kasten, Yoni and Kritchman, Shira},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  year={2019}
}

@article{yong2022autoencoders,
  title={Do autoencoders need a bottleneck for anomaly detection?},
  author={Yong, Bang Xiang and Brintrup, Alexandra},
  journal={arXiv preprint arXiv:2202.12637},
  year={2022}
}

@article{bishop1994novelty,
  title={Novelty detection and neural network validation},
  author={Bishop, Christopher M},
  journal={IEE Proceedings-Vision, Image and Signal processing},
  volume={141},
  number={4},
  pages={217--222},
  year={1994},
  publisher={IET}
}
@techreport{rumelhart1985learning,
  title={Learning internal representations by error propagation},
  author={Rumelhart, David E and Hinton, Geoffrey E and Williams, Ronald J},
  year={1985},
  institution={California Univ San Diego La Jolla Inst for Cognitive Science}
}
@inproceedings{japkowicz1995novelty,
  title={A novelty detection approach to classification},
  author={Japkowicz, Nathalie and Myers, Catherine and Gluck, Mark and others},
  booktitle={IJCAI},
  volume={1},
  pages={518--523},
  year={1995},
  organization={Citeseer}
}