{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:16:41Z","timestamp":1750220201053,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["CCF-1918770, NRT DGE-1545362,OAC-1835660, IIS-1954376, IIS-1815696"],"award-info":[{"award-number":["CCF-1918770, NRT DGE-1545362,OAC-1835660, IIS-1954376, IIS-1815696"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539344","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"283-293","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Framing Algorithmic Recourse for Anomaly Detection"],"prefix":"10.1145","author":[{"given":"Debanjan","family":"Datta","sequence":"first","affiliation":[{"name":"Virginia Tech, Arlington, VA, USA"}]},{"given":"Feng","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Texas, Dallas, Dallas, TX, USA"}]},{"given":"Naren","family":"Ramakrishnan","sequence":"additional","affiliation":[{"name":"Virginia Tech, Arlington, VA, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"volume-title":"Toward explainable deep neural network based anomaly detection. In 2018 11th ICHSI","author":"Amarasinghe Kasun","key":"e_1_3_2_1_1_1","unstructured":"Kasun Amarasinghe, Kevin Kenney, and Milos Manic. 2018. Toward explainable deep neural network based anomaly detection. In 2018 11th ICHSI. IEEE."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115736"},{"volume-title":"Plausible counterfactuals: Auditing deep learning classifiers with realistic adversarial examples. In 2020 IJCNN","author":"Barredo-Arrieta Alejandro","key":"e_1_3_2_1_3_1","unstructured":"Alejandro Barredo-Arrieta and Javier Del Ser. 2020. Plausible counterfactuals: Auditing deep learning classifiers with realistic adversarial examples. In 2020 IJCNN. IEEE."},{"volume-title":"KDD 2017 Workshop on Anomaly Detection in Finance. PMLR.","author":"Bokai","key":"e_1_3_2_1_4_1","unstructured":"Bokai Cao et al. 2018. Collective fraud detection capturing inter-transaction dependency. In KDD 2017 Workshop on Anomaly Detection in Finance. PMLR."},{"key":"e_1_3_2_1_5_1","volume-title":"Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance. arXiv preprint arXiv:2007.11117","author":"Carletti Mattia","year":"2020","unstructured":"Mattia Carletti, Matteo Terzi, and Gian Antonio Susto. 2020. Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance. arXiv preprint arXiv:2007.11117 (2020)."},{"key":"e_1_3_2_1_6_1","unstructured":"Varun Chandola Shyam Boriah and Vipin Kumar. 2007. Similarity Measures for Categorical Data--A Comparative Study. (2007)."},{"key":"e_1_3_2_1_7_1","volume-title":"FIMAP: Feature Importance by Minimal Adversarial Perturbation. In AAAI","author":"Chapman-Rounds Matt","year":"2021","unstructured":"Matt Chapman-Rounds et al. 2021. FIMAP: Feature Importance by Minimal Adversarial Perturbation. In AAAI, Vol. 35."},{"key":"e_1_3_2_1_8_1","unstructured":"Ting Chen et al. 2016. Entity embedding-based anomaly detection for heterogeneous categorical events. In IJCAI."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Riccardo Crupi et al. 2021. Counterfactual Explanations as Interventions in Latent Space. arXiv e-prints (2021) arXiv--2106.","DOI":"10.21203\/rs.3.rs-626875\/v1"},{"volume-title":"International Conference on Parallel Problem Solving from Nature. Springer, 448--469","author":"Susanne","key":"e_1_3_2_1_10_1","unstructured":"Susanne Dandl et al. 2020. Multi-objective counterfactual explanations. In International Conference on Parallel Problem Solving from Nature. Springer, 448--469."},{"key":"e_1_3_2_1_11_1","volume-title":"mbox","author":"Dang Xuan Hong","year":"2013","unstructured":"Xuan Hong Dang et almbox. 2013. Local outlier detection with interpretation. In ECML PKDD. Springer, 304--320."},{"key":"e_1_3_2_1_12_1","volume-title":"IEEE ICDE","author":"Hong Xuan","year":"2014","unstructured":"Xuan Hong Dang et al. 2014. Discriminative features for identifying and interpreting outliers. In IEEE ICDE 2014. IEEE, 88--99."},{"volume-title":"13th ACM SIGKDD. 220--229.","author":"Das Kaustav","key":"e_1_3_2_1_13_1","unstructured":"Kaustav Das and Jeff Schneider. 2007. Detecting anomalous records in categorical datasets. In 13th ACM SIGKDD. 220--229."},{"key":"e_1_3_2_1_14_1","volume-title":"Detecting Suspicious Timber Trades. In Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"Debanjan","unstructured":"Debanjan Datta et al. 2020. Detecting Suspicious Timber Trades. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 13248--13254."},{"key":"e_1_3_2_1_15_1","volume-title":"mbox","author":"Jacob Devlin","year":"2019","unstructured":"Jacob Devlin et almbox. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL. ACL."},{"key":"e_1_3_2_1_16_1","volume-title":"NeurIPS","volume":"31","author":"Amit","year":"2018","unstructured":"Amit Dhurandhar et al. 2018. Explanations based on the missing: Towards contrastive explanations with pertinent negatives. NeurIPS, Vol. 31 (2018)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Swastik Haldar et al. 2021. Reliable Counterfactual Explanations for Autoencoder Based Anomalies. In 8th ACM IKDD CODS and 26th COMAD.","DOI":"10.1145\/3430984.3431015"},{"key":"e_1_3_2_1_18_1","unstructured":"Renjun Hu Charu C Aggarwal Shuai Ma and Jinpeng Huai. 2016. An embedding approach to anomaly detection. In ICDE."},{"key":"e_1_3_2_1_19_1","volume-title":"Tabtransformer: Tabular data modeling using contextual embeddings. arXiv preprint arXiv:2012.06678","author":"Xin Huang","year":"2020","unstructured":"Xin Huang et al. 2020. Tabtransformer: Tabular data modeling using contextual embeddings. arXiv preprint arXiv:2012.06678 (2020)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2019.2921572"},{"key":"e_1_3_2_1_21_1","unstructured":"Shalmali Joshi et al. 2019. Towards realistic individual recourse and actionable explanations in black-box decision making systems. arXiv preprint arXiv:1907.09615 (2019)."},{"key":"e_1_3_2_1_22_1","unstructured":"Amir-Hossein Karimi et al. 2020 a. Model-agnostic counterfactual explanations for consequential decisions. In AISTATS. PMLR 895--905."},{"key":"e_1_3_2_1_23_1","volume-title":"mbox. 2020 b. A survey of algorithmic recourse: definitions, formulations, solutions, and prospects. CoRR","author":"Karimi Amir-Hossein","year":"2020","unstructured":"Amir-Hossein Karimi et almbox. 2020 b. A survey of algorithmic recourse: definitions, formulations, solutions, and prospects. CoRR, Vol. abs\/2010.04050 (2020)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Amir-Hossein Karimi Bernhard Sch\u00f6lkopf and Isabel Valera. 2021. Algorithmic recourse: from counterfactual explanations to interventions. In ACM FaccT.","DOI":"10.1145\/3442188.3445899"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107198"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58342-2_11"},{"key":"e_1_3_2_1_27_1","volume-title":"NeurIPS","volume":"30","author":"Lundberg Scott M","year":"2017","unstructured":"Scott M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In NeurIPS 2017, Vol. 30."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-018-0585-7"},{"key":"e_1_3_2_1_29_1","unstructured":"Divyat Mahajan et al. 2019. Preserving causal constraints in counterfactual explanations for machine learning classifiers. arXiv preprint arXiv:1912.03277 (2019)."},{"key":"e_1_3_2_1_30_1","volume-title":"ACM FAT","author":"Ramaravind","year":"2020","unstructured":"Ramaravind K Mothilal et al. 2020. Explaining machine learning classifiers through diverse counterfactual explanations. In ACM FAT 2020."},{"key":"e_1_3_2_1_31_1","volume-title":"Gee: A gradient-based explainable variational autoencoder for network anomaly detection. In 2019 IEEE CNS. 91--99.","author":"Nguyen Quoc Phong","year":"2019","unstructured":"Quoc Phong Nguyen et al. 2019. Gee: A gradient-based explainable variational autoencoder for network anomaly detection. In 2019 IEEE CNS. 91--99."},{"key":"e_1_3_2_1_32_1","volume-title":"Interpretml: A unified framework for machine learning interpretability. arXiv preprint arXiv:1909.09223","author":"Harsha Nori","year":"2019","unstructured":"Harsha Nori et al. 2019. Interpretml: A unified framework for machine learning interpretability. arXiv preprint arXiv:1909.09223 (2019)."},{"key":"e_1_3_2_1_33_1","unstructured":"Panjiva. 2019. Panjiva Trade Data. https:\/\/panjiva.com."},{"key":"e_1_3_2_1_34_1","volume-title":"The Web Conference","author":"Martin","year":"2020","unstructured":"Martin Pawelczyk et al. 2020. Learning model-agnostic counterfactual explanations for tabular data. In The Web Conference 2020. 3126--3132."},{"key":"e_1_3_2_1_35_1","volume-title":"NMI","volume":"2","author":"Mattia","year":"2020","unstructured":"Mattia Prosperi et al. 2020. Causal inference and counterfactual prediction in machine learning for actionable healthcare. NMI, Vol. 2, 7 (2020)."},{"key":"e_1_3_2_1_36_1","volume-title":"Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses. NeurIPS","author":"Rawal Kaivalya","year":"2020","unstructured":"Kaivalya Rawal and Himabindu Lakkaraju. 2020. Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses. NeurIPS (2020)."},{"volume-title":"22nd ACM SIGKDD .","author":"Ribeiro Marco Tulio","key":"e_1_3_2_1_37_1","unstructured":"Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. \"Why should i trust you?\" Explaining the predictions of any classifier. In 22nd ACM SIGKDD ."},{"key":"e_1_3_2_1_38_1","volume-title":"Certifai: Counterfactual explanations for robustness, transparency, interpretability, and fairness of artificial intelligence models. arXiv preprint arXiv:1905.07857","author":"Sharma Shubham","year":"2019","unstructured":"Shubham Sharma, Jette Henderson, and Joydeep Ghosh. 2019. Certifai: Counterfactual explanations for robustness, transparency, interpretability, and fairness of artificial intelligence models. arXiv preprint arXiv:1905.07857 (2019)."},{"key":"e_1_3_2_1_39_1","volume-title":"International conference on machine learning. PMLR, 3145--3153","author":"Shrikumar Avanti","year":"2017","unstructured":"Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017. Learning important features through propagating activation differences. In International conference on machine learning. PMLR, 3145--3153."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402736"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Berk Ustun Alexander Spangher and Yang Liu. 2019. Actionable recourse in linear classification. In ACM FAT. 10--19.","DOI":"10.1145\/3287560.3287566"},{"key":"e_1_3_2_1_42_1","unstructured":"Ashish Vaswani et al. 2017. Attention is all you need. In NeurIPS. 5998--6008."},{"key":"e_1_3_2_1_43_1","first-page":"841","article-title":"Counterfactual explanations without opening the black box: Automated decisions and the GDPR","volume":"31","author":"Sandra Wachter","year":"2017","unstructured":"Sandra Wachter et al. 2017. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harv. JL & Tech., Vol. 31 (2017), 841.","journal-title":"Harv. JL & Tech."},{"key":"e_1_3_2_1_44_1","volume-title":"mbox","author":"Quan Wang","year":"2017","unstructured":"Quan Wang et almbox. 2017. Knowledge graph embedding: A survey of approaches and applications. IEEE TKDE 12 (2017)."},{"key":"e_1_3_2_1_45_1","unstructured":"Prateek Yadav et al. 2021. Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions. arXiv preprint arXiv:2111.01235 (2021)."},{"key":"e_1_3_2_1_46_1","unstructured":"Bishan Yang et al. 2014. Embedding entities and relations for learning and inference in knowledge bases. ICLR."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2021.101946"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"crossref","unstructured":"Xiao Zhang et al. 2019. ACE--an anomaly contribution explainer for cyber-security applications. In 2019 IEEE Big Data. IEEE 1991--2000.","DOI":"10.1109\/BigData47090.2019.9005989"}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Washington DC USA","acronym":"KDD '22"},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539344","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539344","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539344","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:47Z","timestamp":1750186967000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539344"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":48,"alternative-id":["10.1145\/3534678.3539344","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539344","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}