{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T05:06:09Z","timestamp":1778389569720,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":75,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China","award":["72271151"],"award-info":[{"award-number":["72271151"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,6]]},"DOI":"10.1145\/3580305.3599258","type":"proceedings-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T18:10:58Z","timestamp":1691172658000},"page":"984-996","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Anomaly Detection with Score Distribution Discrimination"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1285-0208","authenticated-orcid":false,"given":"Minqi","family":"Jiang","sequence":"first","affiliation":[{"name":"Shanghai University of Finance and Economics, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2896-0607","authenticated-orcid":false,"given":"Songqiao","family":"Han","sequence":"additional","affiliation":[{"name":"Shanghai University of Finance and Economics, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0009-6677","authenticated-orcid":false,"given":"Hailiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Shanghai University of Finance and Economics, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Pair-copula constructions of multiple dependence. Insurance: Mathematics and economics","author":"Aas Kjersti","year":"2009","unstructured":"Kjersti Aas , Claudia Czado , Arnoldo Frigessi , and Henrik Bakken . 2009. Pair-copula constructions of multiple dependence. Insurance: Mathematics and economics , Vol. 44 , 2 ( 2009 ), 182--198. Kjersti Aas, Claudia Czado, Arnoldo Frigessi, and Henrik Bakken. 2009. Pair-copula constructions of multiple dependence. Insurance: Mathematics and economics, Vol. 44, 2 (2009), 182--198."},{"key":"e_1_3_2_2_2_1","volume-title":"Outlier analysis","author":"Aggarwal Charu C","unstructured":"Charu C Aggarwal . 2017. An introduction to outlier analysis . In Outlier analysis . Springer , 1--34. Charu C Aggarwal. 2017. An introduction to outlier analysis. In Outlier analysis. Springer, 1--34."},{"key":"e_1_3_2_2_3_1","volume-title":"Asian conference on computer vision. Springer, 622--637","author":"Akcay Samet","year":"2018","unstructured":"Samet Akcay , Amir Atapour-Abarghouei , and Toby P Breckon . 2018 . Ganomaly: Semi-supervised anomaly detection via adversarial training . In Asian conference on computer vision. Springer, 622--637 . Samet Akcay, Amir Atapour-Abarghouei, and Toby P Breckon. 2018. Ganomaly: Semi-supervised anomaly detection via adversarial training. In Asian conference on computer vision. Springer, 622--637."},{"key":"e_1_3_2_2_4_1","volume-title":"2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 1--8.","author":"Samet Akcc","year":"2019","unstructured":"Samet Akcc ay, Amir Atapour-Abarghouei , and Toby P Breckon . 2019 . Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection . In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 1--8. Samet Akcc ay, Amir Atapour-Abarghouei, and Toby P Breckon. 2019. Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection. In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 1--8."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_9"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3230833.3232818"},{"key":"e_1_3_2_2_7_1","volume-title":"Proceedings of ICML workshop on unsupervised and transfer learning. JMLR Workshop and Conference Proceedings, 37--49","author":"Baldi Pierre","year":"2012","unstructured":"Pierre Baldi . 2012 . Autoencoders, unsupervised learning, and deep architectures . In Proceedings of ICML workshop on unsupervised and transfer learning. JMLR Workshop and Conference Proceedings, 37--49 . Pierre Baldi. 2012. Autoencoders, unsupervised learning, and deep architectures. In Proceedings of ICML workshop on unsupervised and transfer learning. JMLR Workshop and Conference Proceedings, 37--49."},{"key":"e_1_3_2_2_8_1","volume-title":"Probability and measure","author":"Billingsley Patrick","unstructured":"Patrick Billingsley . 2008. Probability and measure . John Wiley & Sons . Patrick Billingsley. 2008. Probability and measure. John Wiley & Sons."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Markus M Breunig Hans-Peter Kriegel Raymond T Ng and J\u00f6rg Sander. 2000. LOF: identifying density-based local outliers. In SIGMOD. 93--104.  Markus M Breunig Hans-Peter Kriegel Raymond T Ng and J\u00f6rg Sander. 2000. LOF: identifying density-based local outliers. In SIGMOD. 93--104.","DOI":"10.1145\/335191.335388"},{"key":"e_1_3_2_2_10_1","unstructured":"Richard L Burden J Douglas Faires and Annette M Burden. 2015. Numerical analysis. Cengage learning.  Richard L Burden J Douglas Faires and Annette M Burden. 2015. Numerical analysis. Cengage learning."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_17"},{"key":"e_1_3_2_2_12_1","volume-title":"Hendrik Paul Lopuha\u00e4, and Ludolf Erwin Meester","author":"Dekking Frederik Michel","year":"2005","unstructured":"Frederik Michel Dekking , Cornelis Kraaikamp , Hendrik Paul Lopuha\u00e4, and Ludolf Erwin Meester . 2005 . A Modern Introduction to Probability and Statistics: Understanding why and how. Vol. 488 . Springer . Frederik Michel Dekking, Cornelis Kraaikamp, Hendrik Paul Lopuha\u00e4, and Ludolf Erwin Meester. 2005. A Modern Introduction to Probability and Statistics: Understanding why and how. Vol. 488. Springer."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3363226"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.5555\/3322706.3361996"},{"key":"e_1_3_2_2_15_1","volume-title":"ArXiv","volume":"1503","author":"Emmott Andrew","year":"2015","unstructured":"Andrew Emmott , Shubhomoy Das , Thomas Dietterich , Alan Fern , and Weng-Keen Wong . 2015 . A meta-analysis of the anomaly detection problem . ArXiv , Vol. 1503 .01158 (2015). https:\/\/arxiv.org\/abs\/1503.01158 Andrew Emmott, Shubhomoy Das, Thomas Dietterich, Alan Fern, and Weng-Keen Wong. 2015. A meta-analysis of the anomaly detection problem. ArXiv, Vol. 1503.01158 (2015). https:\/\/arxiv.org\/abs\/1503.01158"},{"key":"e_1_3_2_2_16_1","volume-title":"International Conference on Learning Representations.","author":"Camara Gomes Eduardo Dadalto","year":"2021","unstructured":"Eduardo Dadalto Camara Gomes , Florence Alberge , Pierre Duhamel , and Pablo Piantanida . 2021 . Igeood: An Information Geometry Approach to Out-of-Distribution Detection . In International Conference on Learning Representations. Eduardo Dadalto Camara Gomes, Florence Alberge, Pierre Duhamel, and Pablo Piantanida. 2021. Igeood: An Information Geometry Approach to Out-of-Distribution Detection. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_17_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Gopalan Parikshit","year":"2019","unstructured":"Parikshit Gopalan , Vatsal Sharan , and Udi Wieder . 2019 . Pidforest: anomaly detection via partial identification . Advances in Neural Information Processing Systems , Vol. 32 (2019). Parikshit Gopalan, Vatsal Sharan, and Udi Wieder. 2019. Pidforest: anomaly detection via partial identification. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_18_1","first-page":"18932","article-title":"Revisiting deep learning models for tabular data","volume":"34","author":"Gorishniy Yury","year":"2021","unstructured":"Yury Gorishniy , Ivan Rubachev , Valentin Khrulkov , and Artem Babenko . 2021 . Revisiting deep learning models for tabular data . Advances in Neural Information Processing Systems , Vol. 34 (2021), 18932 -- 18943 . Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko. 2021. Revisiting deep learning models for tabular data. Advances in Neural Information Processing Systems, Vol. 34 (2021), 18932--18943.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/2512538.2512545"},{"key":"e_1_3_2_2_20_1","volume-title":"Semi-supervised learning by entropy minimization. Advances in neural information processing systems","author":"Grandvalet Yves","year":"2004","unstructured":"Yves Grandvalet and Yoshua Bengio . 2004. Semi-supervised learning by entropy minimization. Advances in neural information processing systems , Vol. 17 ( 2004 ). Yves Grandvalet and Yoshua Bengio. 2004. Semi-supervised learning by entropy minimization. Advances in neural information processing systems, Vol. 17 (2004)."},{"key":"e_1_3_2_2_21_1","volume-title":"ADBench: Anomaly Detection Benchmark. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track.","author":"Han Songqiao","unstructured":"Songqiao Han , Xiyang Hu , Hailiang Huang , Minqi Jiang , and Yue Zhao . [n.,d.]. ADBench: Anomaly Detection Benchmark. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track. Songqiao Han, Xiyang Hu, Hailiang Huang, Minqi Jiang, and Yue Zhao. [n.,d.]. ADBench: Anomaly Detection Benchmark. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track."},{"key":"e_1_3_2_2_22_1","volume-title":"Discovering cluster-based local outliers. Pattern recognition letters","author":"He Zengyou","year":"2003","unstructured":"Zengyou He , Xiaofei Xu , and Shengchun Deng . 2003. Discovering cluster-based local outliers. Pattern recognition letters , Vol. 24 , 9--10 ( 2003 ), 1641--1650. Zengyou He, Xiaofei Xu, and Shengchun Deng. 2003. Discovering cluster-based local outliers. Pattern recognition letters, Vol. 24, 9--10 (2003), 1641--1650."},{"key":"e_1_3_2_2_23_1","volume-title":"A review of anomaly detection techniques and applications in financial fraud. Expert Systems with Applications","author":"Hilal Waleed","year":"2021","unstructured":"Waleed Hilal , S Andrew Gadsden , and John Yawney . 2021. A review of anomaly detection techniques and applications in financial fraud. Expert Systems with Applications ( 2021 ), 116429. Waleed Hilal, S Andrew Gadsden, and John Yawney. 2021. A review of anomaly detection techniques and applications in financial fraud. Expert Systems with Applications (2021), 116429."},{"key":"e_1_3_2_2_24_1","volume-title":"Enhancing Unsupervised Anomaly Detection with Score-Guided Network. arXiv preprint arXiv:2109.04684","author":"Huang Zongyuan","year":"2021","unstructured":"Zongyuan Huang , Baohua Zhang , Guoqiang Hu , Longyuan Li , Yanyan Xu , and Yaohui Jin . 2021. Enhancing Unsupervised Anomaly Detection with Score-Guided Network. arXiv preprint arXiv:2109.04684 ( 2021 ). Zongyuan Huang, Baohua Zhang, Guoqiang Hu, Longyuan Li, Yanyan Xu, and Yaohui Jin. 2021. Enhancing Unsupervised Anomaly Detection with Score-Guided Network. arXiv preprint arXiv:2109.04684 (2021)."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1080\/03610928908830127"},{"key":"e_1_3_2_2_26_1","volume-title":"International Conference on Learning Representations.","author":"Jiang Dihong","year":"2021","unstructured":"Dihong Jiang , Sun Sun , and Yaoliang Yu . 2021 . Revisiting flow generative models for Out-of-distribution detection . In International Conference on Learning Representations. Dihong Jiang, Sun Sun, and Yaoliang Yu. 2021. Revisiting flow generative models for Out-of-distribution detection. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_27_1","volume-title":"International Conference on Learning Representations.","author":"Kim Taesung","year":"2021","unstructured":"Taesung Kim , Jinhee Kim , Yunwon Tae , Cheonbok Park , Jang-Ho Choi , and Jaegul Choo . 2021 . Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift . In International Conference on Learning Representations. Taesung Kim, Jinhee Kim, Yunwon Tae, Cheonbok Park, Jang-Ho Choi, and Jaegul Choo. 2021. Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_28_1","volume-title":"Kingma and Max Welling","author":"Diederik","year":"2014","unstructured":"Diederik P. Kingma and Max Welling . 2014 . Auto-Encoding Variational Bayes. In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds .). Diederik P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.)."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"e_1_3_2_2_30_1","volume-title":"A comparative study of anomaly detection schemes in network intrusion detection","author":"Lazarevic Aleksandar","unstructured":"Aleksandar Lazarevic , Levent Ertoz , Vipin Kumar , Aysel Ozgur , and Jaideep Srivastava . 2003. A comparative study of anomaly detection schemes in network intrusion detection . In SDM. SIAM , 25--36. Aleksandar Lazarevic, Levent Ertoz, Vipin Kumar, Aysel Ozgur, and Jaideep Srivastava. 2003. A comparative study of anomaly detection schemes in network intrusion detection. In SDM. SIAM, 25--36."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00726"},{"key":"e_1_3_2_2_32_1","volume-title":"Big Data","author":"Lee Meng-Chieh","unstructured":"Meng-Chieh Lee , Shubhranshu Shekhar , Christos Faloutsos , T Noah Hutson , and Leon Iasemidis . 2021a. Gen 2 Out: Detecting and Ranking Generalized Anomalies . In Big Data . IEEE , 801--811. Meng-Chieh Lee, Shubhranshu Shekhar, Christos Faloutsos, T Noah Hutson, and Leon Iasemidis. 2021a. Gen 2 Out: Detecting and Ranking Generalized Anomalies. In Big Data. IEEE, 801--811."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00210"},{"key":"e_1_3_2_2_34_1","volume-title":"Dual-MGAN: An Efficient Approach for Semi-supervised Outlier Detection with Few Identified Anomalies. ACM Transactions on Knowledge Discovery from Data (TKDD)","author":"Li Zhe","year":"2022","unstructured":"Zhe Li , Chunhua Sun , Chunli Liu , Xiayu Chen , Meng Wang , and Yezheng Liu . 2022a. Dual-MGAN: An Efficient Approach for Semi-supervised Outlier Detection with Few Identified Anomalies. ACM Transactions on Knowledge Discovery from Data (TKDD) ( 2022 ). Zhe Li, Chunhua Sun, Chunli Liu, Xiayu Chen, Meng Wang, and Yezheng Liu. 2022a. Dual-MGAN: An Efficient Approach for Semi-supervised Outlier Detection with Few Identified Anomalies. ACM Transactions on Knowledge Discovery from Data (TKDD) (2022)."},{"key":"e_1_3_2_2_35_1","volume-title":"Ecod: Unsupervised outlier detection using empirical cumulative distribution functions","author":"Li Zheng","year":"2022","unstructured":"Zheng Li , Yue Zhao , Xiyang Hu , Nicola Botta , Cezar Ionescu , and George Chen . 2022 b. Ecod: Unsupervised outlier detection using empirical cumulative distribution functions . IEEE Transactions on Knowledge and Data Engineering ( 2022). Zheng Li, Yue Zhao, Xiyang Hu, Nicola Botta, Cezar Ionescu, and George Chen. 2022b. Ecod: Unsupervised outlier detection using empirical cumulative distribution functions. IEEE Transactions on Knowledge and Data Engineering (2022)."},{"key":"e_1_3_2_2_36_1","volume-title":"Unsupervised Anomaly Detection by Robust Density Estimation","author":"Liu Boyang","year":"2032","unstructured":"Boyang Liu , Pang-Ning Tan , and Jiayu Zhou . 2022. Unsupervised Anomaly Detection by Robust Density Estimation . In AAAI. AAAI Press , 4101--4108. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/ 2032 8 Boyang Liu, Pang-Ning Tan, and Jiayu Zhou. 2022. Unsupervised Anomaly Detection by Robust Density Estimation. In AAAI. AAAI Press, 4101--4108. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/20328"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_2"},{"key":"e_1_3_2_2_38_1","volume-title":"Kai Ming Ting, and Zhi-Hua Zhou","author":"Liu Fei Tony","year":"2008","unstructured":"Fei Tony Liu , Kai Ming Ting, and Zhi-Hua Zhou . 2008 . Isolation forest. In 2008 eighth ieee international conference on data mining. IEEE , 413--422. Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2008. Isolation forest. In 2008 eighth ieee international conference on data mining. IEEE, 413--422."},{"key":"e_1_3_2_2_39_1","volume-title":"International Conference on Learning Representations.","author":"Liu Siyan","year":"2021","unstructured":"Siyan Liu , Pei Zhang , Dan Lu , and Guannan Zhang . 2021 . PI3NN: Out-of-distribution-aware Prediction Intervals from Three Neural Networks . In International Conference on Learning Representations. Siyan Liu, Pei Zhang, Dan Lu, and Guannan Zhang. 2021. PI3NN: Out-of-distribution-aware Prediction Intervals from Three Neural Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2905606"},{"key":"e_1_3_2_2_41_1","volume-title":"Deep Learning for Hate Speech Detection: A Comparative Study. arXiv preprint arXiv:2202.09517","author":"Malik Jitendra Singh","year":"2022","unstructured":"Jitendra Singh Malik , Guansong Pang , and Anton van den Hengel . 2022. Deep Learning for Hate Speech Detection: A Comparative Study. arXiv preprint arXiv:2202.09517 ( 2022 ). Jitendra Singh Malik, Guansong Pang, and Anton van den Hengel. 2022. Deep Learning for Hate Speech Detection: A Comparative Study. arXiv preprint arXiv:2202.09517 (2022)."},{"key":"e_1_3_2_2_42_1","unstructured":"Rafael Martinez-Guerra and Juan Luis Mata-Machuca. 2016. Fault detection and diagnosis in nonlinear systems.  Rafael Martinez-Guerra and Juan Luis Mata-Machuca. 2016. Fault detection and diagnosis in nonlinear systems."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02294153"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.5120\/13715-1478"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220042"},{"key":"e_1_3_2_2_46_1","volume-title":"Explainable Deep Few-shot Anomaly Detection with Deviation Networks. arXiv preprint arXiv:2108.00462","author":"Pang Guansong","year":"2021","unstructured":"Guansong Pang , Choubo Ding , Chunhua Shen , and Anton van den Hengel . 2021. Explainable Deep Few-shot Anomaly Detection with Deviation Networks. arXiv preprint arXiv:2108.00462 ( 2021 ). Guansong Pang, Choubo Ding, Chunhua Shen, and Anton van den Hengel. 2021. Explainable Deep Few-shot Anomaly Detection with Deviation Networks. arXiv preprint arXiv:2108.00462 (2021)."},{"key":"e_1_3_2_2_47_1","volume-title":"Deep weakly-supervised anomaly detection. arXiv preprint arXiv:1910.13601","author":"Pang Guansong","year":"2019","unstructured":"Guansong Pang , Chunhua Shen , Huidong Jin , and Anton van den Hengel . 2019b. Deep weakly-supervised anomaly detection. arXiv preprint arXiv:1910.13601 ( 2019 ). Guansong Pang, Chunhua Shen, Huidong Jin, and Anton van den Hengel. 2019b. Deep weakly-supervised anomaly detection. arXiv preprint arXiv:1910.13601 (2019)."},{"key":"e_1_3_2_2_48_1","volume-title":"Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 353--362","author":"Pang Guansong","unstructured":"Guansong Pang , Chunhua Shen , and Anton van den Hengel. 2019a. Deep anomaly detection with deviation networks . In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 353--362 . Guansong Pang, Chunhua Shen, and Anton van den Hengel. 2019a. Deep anomaly detection with deviation networks. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 353--362."},{"key":"e_1_3_2_2_49_1","volume-title":"Lei Jimmy Ba, and Ruslan Salakhutdinov","author":"Parisotto Emilio","year":"2016","unstructured":"Emilio Parisotto , Lei Jimmy Ba, and Ruslan Salakhutdinov . 2016 . Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning. In ICLR (Poster) . Emilio Parisotto, Lei Jimmy Ba, and Ruslan Salakhutdinov. 2016. Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning. In ICLR (Poster)."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2020.113303"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974010.43"},{"key":"e_1_3_2_2_52_1","volume-title":"Gaussian mixture models. Encyclopedia of biometrics","author":"Reynolds Douglas A","year":"2009","unstructured":"Douglas A Reynolds . 2009. Gaussian mixture models. Encyclopedia of biometrics , Vol. 741 , 659--663 ( 2009 ). Douglas A Reynolds. 2009. Gaussian mixture models. Encyclopedia of biometrics, Vol. 741, 659--663 (2009)."},{"key":"e_1_3_2_2_53_1","volume-title":"Piotr Dollar, and Lubomir Bourdev","author":"Rippel Oren","year":"2015","unstructured":"Oren Rippel , Manohar Paluri , Piotr Dollar, and Lubomir Bourdev . 2015 . Metric learning with adaptive density discrimination. arXiv preprint arXiv:1511.05939 (2015). Oren Rippel, Manohar Paluri, Piotr Dollar, and Lubomir Bourdev. 2015. Metric learning with adaptive density discrimination. arXiv preprint arXiv:1511.05939 (2015)."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3052449"},{"key":"e_1_3_2_2_55_1","volume-title":"International conference on machine learning. 4393--4402","author":"Ruff Lukas","year":"2018","unstructured":"Lukas Ruff , Robert Vandermeulen , Nico Goernitz , Lucas Deecke , Shoaib Ahmed Siddiqui , Alexander Binder , Emmanuel M\u00fcller , and Marius Kloft . 2018 . Deep one-class classification . In International conference on machine learning. 4393--4402 . Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel M\u00fcller, and Marius Kloft. 2018. Deep one-class classification. In International conference on machine learning. 4393--4402."},{"key":"e_1_3_2_2_56_1","volume-title":"Deep Semi-Supervised Anomaly Detection. In 8th International Conference on Learning Representations, ICLR 2020","author":"Ruff Lukas","year":"2020","unstructured":"Lukas Ruff , Robert A. Vandermeulen , Nico G\u00f6rnitz , Alexander Binder , Emmanuel M\u00fc ller, Klaus-Robert M\u00fcller , and Marius Kloft . 2020 . Deep Semi-Supervised Anomaly Detection. In 8th International Conference on Learning Representations, ICLR 2020 , Addis Ababa, Ethiopia , April 26-30, 2020. OpenReview.net. Lukas Ruff, Robert A. Vandermeulen, Nico G\u00f6rnitz, Alexander Binder, Emmanuel M\u00fc ller, Klaus-Robert M\u00fcller, and Marius Kloft. 2020. Deep Semi-Supervised Anomaly Detection. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net."},{"key":"e_1_3_2_2_57_1","volume-title":"Proceedings of the KDD'21 Workshop on Outlier Detection and Description. Outlier Detection and Description Organising Committee, 1--9.","author":"Soenen Jonas","year":"2021","unstructured":"Jonas Soenen , Elia Van Wolputte , Lorenzo Perini , Vincent Vercruyssen , Wannes Meert , Jesse Davis , and Hendrik Blockeel . 2021 . The Effect of Hyperparameter Tuning on the Comparative Evaluation of Unsupervised Anomaly Detection Methods . In Proceedings of the KDD'21 Workshop on Outlier Detection and Description. Outlier Detection and Description Organising Committee, 1--9. Jonas Soenen, Elia Van Wolputte, Lorenzo Perini, Vincent Vercruyssen, Wannes Meert, Jesse Davis, and Hendrik Blockeel. 2021. The Effect of Hyperparameter Tuning on the Comparative Evaluation of Unsupervised Anomaly Detection Methods. In Proceedings of the KDD'21 Workshop on Outlier Detection and Description. Outlier Detection and Description Organising Committee, 1--9."},{"key":"e_1_3_2_2_58_1","volume-title":"Benchmarking unsupervised outlier detection with realistic synthetic data. ACM Transactions on Knowledge Discovery from Data (TKDD)","author":"Steinbuss Georg","year":"2021","unstructured":"Georg Steinbuss and Klemens B\u00f6hm . 2021. Benchmarking unsupervised outlier detection with realistic synthetic data. ACM Transactions on Knowledge Discovery from Data (TKDD) , Vol. 15 , 4 ( 2021 ), 1--20. Georg Steinbuss and Klemens B\u00f6hm. 2021. Benchmarking unsupervised outlier detection with realistic synthetic data. ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 15, 4 (2021), 1--20."},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00678"},{"key":"e_1_3_2_2_60_1","article-title":"Visualizing data using t-SNE","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton . 2008 . Visualizing data using t-SNE . Journal of machine learning research , Vol. 9 , 11 (2008). Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research, Vol. 9, 11 (2008).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00535"},{"key":"e_1_3_2_2_62_1","volume-title":"Wilcoxon signed-rank test","author":"Woolson Robert F","year":"2007","unstructured":"Robert F Woolson . 2007. Wilcoxon signed-rank test . Wiley encyclopedia of clinical trials ( 2007 ), 1--3. Robert F Woolson. 2007. Wilcoxon signed-rank test. Wiley encyclopedia of clinical trials (2007), 1--3."},{"key":"e_1_3_2_2_63_1","first-page":"17009","article-title":"Design space for graph neural networks","volume":"33","author":"You Jiaxuan","year":"2020","unstructured":"Jiaxuan You , Zhitao Ying , and Jure Leskovec . 2020 . Design space for graph neural networks . Advances in Neural Information Processing Systems , Vol. 33 (2020), 17009 -- 17021 . Jiaxuan You, Zhitao Ying, and Jure Leskovec. 2020. Design space for graph neural networks. Advances in Neural Information Processing Systems, Vol. 33 (2020), 17009--17021.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_64_1","first-page":"5824","article-title":"Gradient surgery for multi-task learning","volume":"33","author":"Yu Tianhe","year":"2020","unstructured":"Tianhe Yu , Saurabh Kumar , Abhishek Gupta , Sergey Levine , Karol Hausman , and Chelsea Finn . 2020 . Gradient surgery for multi-task learning . Advances in Neural Information Processing Systems , Vol. 33 (2020), 5824 -- 5836 . Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn. 2020. Gradient surgery for multi-task learning. Advances in Neural Information Processing Systems, Vol. 33 (2020), 5824--5836.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_65_1","volume-title":"Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar.","author":"Zenati Houssam","year":"2018","unstructured":"Houssam Zenati , Chuan Sheng Foo , Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar. 2018 a. Efficient gan-based anomaly detection. arXiv preprint arXiv:1802.06222 (2018). Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar. 2018a. Efficient gan-based anomaly detection. arXiv preprint arXiv:1802.06222 (2018)."},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00088"},{"key":"e_1_3_2_2_67_1","volume-title":"International Conference on Machine Learning. PMLR, 3987--3995","author":"Zenke Friedemann","year":"2017","unstructured":"Friedemann Zenke , Ben Poole , and Surya Ganguli . 2017 . Continual learning through synaptic intelligence . In International Conference on Machine Learning. PMLR, 3987--3995 . Friedemann Zenke, Ben Poole, and Surya Ganguli. 2017. Continual learning through synaptic intelligence. In International Conference on Machine Learning. PMLR, 3987--3995."},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2017.10.009"},{"key":"e_1_3_2_2_69_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.03.113"},{"key":"e_1_3_2_2_70_1","first-page":"463","article-title":"SUOD: Accelerating large-scale unsupervised heterogeneous outlier detection","volume":"3","author":"Zhao Yue","year":"2021","unstructured":"Yue Zhao , Xiyang Hu , Cheng Cheng , Cong Wang , Changlin Wan , Wen Wang , Jianing Yang , Haoping Bai , Zheng Li , Cao Xiao , 2021 . SUOD: Accelerating large-scale unsupervised heterogeneous outlier detection . MLSys , Vol. 3 (2021), 463 -- 478 . Yue Zhao, Xiyang Hu, Cheng Cheng, Cong Wang, Changlin Wan, Wen Wang, Jianing Yang, Haoping Bai, Zheng Li, Cao Xiao, et al. 2021. SUOD: Accelerating large-scale unsupervised heterogeneous outlier detection. MLSys, Vol. 3 (2021), 463--478.","journal-title":"MLSys"},{"key":"e_1_3_2_2_71_1","first-page":"1","article-title":"PyOD: A Python Toolbox for Scalable Outlier Detection","volume":"20","author":"Zhao Yue","year":"2019","unstructured":"Yue Zhao , Zain Nasrullah , and Zheng Li . 2019 . PyOD: A Python Toolbox for Scalable Outlier Detection . Journal of Machine Learning Research , Vol. 20 (2019), 1 -- 7 . Yue Zhao, Zain Nasrullah, and Zheng Li. 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of Machine Learning Research, Vol. 20 (2019), 1--7.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_72_1","volume-title":"Admoe: Anomaly detection with mixture-of-experts from noisy labels. arXiv preprint arXiv:2208.11290","author":"Zhao Yue","year":"2022","unstructured":"Yue Zhao , Guoqing Zheng , Subhabrata Mukherjee , Robert McCann , and Ahmed Awadallah . 2022 . Admoe: Anomaly detection with mixture-of-experts from noisy labels. arXiv preprint arXiv:2208.11290 (2022). Yue Zhao, Guoqing Zheng, Subhabrata Mukherjee, Robert McCann, and Ahmed Awadallah. 2022. Admoe: Anomaly detection with mixture-of-experts from noisy labels. arXiv preprint arXiv:2208.11290 (2022)."},{"key":"e_1_3_2_2_73_1","volume-title":"Feature encoding with autoencoders for weakly supervised anomaly detection","author":"Zhou Yingjie","year":"2021","unstructured":"Yingjie Zhou , Xucheng Song , Yanru Zhang , Fanxing Liu , Ce Zhu , and Lingqiao Liu . 2021. Feature encoding with autoencoders for weakly supervised anomaly detection . IEEE Transactions on Neural Networks and Learning Systems ( 2021 ). Yingjie Zhou, Xucheng Song, Yanru Zhang, Fanxing Liu, Ce Zhu, and Lingqiao Liu. 2021. Feature encoding with autoencoders for weakly supervised anomaly detection. IEEE Transactions on Neural Networks and Learning Systems (2021)."},{"key":"e_1_3_2_2_74_1","volume-title":"A brief introduction to weakly supervised learning. National science review","author":"Zhou Zhi-Hua","year":"2018","unstructured":"Zhi-Hua Zhou . 2018. A brief introduction to weakly supervised learning. National science review , Vol. 5 , 1 ( 2018 ), 44--53. Zhi-Hua Zhou. 2018. A brief introduction to weakly supervised learning. National science review, Vol. 5, 1 (2018), 44--53."},{"key":"e_1_3_2_2_75_1","volume-title":"International conference on learning representations.","author":"Zong Bo","year":"2018","unstructured":"Bo Zong , Qi Song , Martin Renqiang Min , Wei Cheng , Cristian Lumezanu , Daeki Cho , and Haifeng Chen . 2018 . Deep autoencoding gaussian mixture model for unsupervised anomaly detection . In International conference on learning representations. Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. 2018. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In International conference on learning representations."}],"event":{"name":"KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Long Beach CA USA","acronym":"KDD '23","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599258","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580305.3599258","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:15Z","timestamp":1750182675000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599258"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":75,"alternative-id":["10.1145\/3580305.3599258","10.1145\/3580305"],"URL":"https:\/\/doi.org\/10.1145\/3580305.3599258","relation":{},"subject":[],"published":{"date-parts":[[2023,8,4]]},"assertion":[{"value":"2023-08-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}