{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T22:14:24Z","timestamp":1784585664088,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1742702,1820609,1909702,1915801,1934782"],"award-info":[{"award-number":["1742702,1820609,1909702,1915801,1934782"]}],"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":[[2020,8,23]]},"DOI":"10.1145\/3394486.3403066","type":"proceedings-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T23:17:27Z","timestamp":1597965447000},"page":"238-248","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":40,"title":["GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction"],"prefix":"10.1145","author":[{"given":"Thai","family":"Le","sequence":"first","affiliation":[{"name":"The Pennsylvania State University, State College, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suhang","family":"Wang","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University, State College, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongwon","family":"Lee","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University, State College, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"TabNet: Attentive Interpretable Tabular Learning. arXiv preprint arXiv:1908.07442","author":"Arik Sercan O","year":"2019","unstructured":"Sercan O Arik and Tomas Pfister . 2019. TabNet: Attentive Interpretable Tabular Learning. arXiv preprint arXiv:1908.07442 ( 2019 ). Sercan O Arik and Tomas Pfister. 2019. TabNet: Attentive Interpretable Tabular Learning. arXiv preprint arXiv:1908.07442 (2019)."},{"key":"e_1_3_2_2_2_1","volume-title":"Deep Learning on Small Datasets without Pre-Training using Cosine Loss. arXiv preprint arXiv:1901.09054","author":"Barz Bj\u00f6rn","year":"2019","unstructured":"Bj\u00f6rn Barz and Joachim Denzler . 2019. Deep Learning on Small Datasets without Pre-Training using Cosine Loss. arXiv preprint arXiv:1901.09054 ( 2019 ). Bj\u00f6rn Barz and Joachim Denzler. 2019. Deep Learning on Small Datasets without Pre-Training using Cosine Loss. arXiv preprint arXiv:1901.09054 (2019)."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.14585"},{"key":"e_1_3_2_2_4_1","unstructured":"Lingyang Chu Xia Hu Juhua Hu Lanjun Wang and Jian Pei. 2018. Exact and consistent interpretation for piecewise linear neural networks: A closed form solution. In ACM SIGKDD\/KDD. ACM 1244--1253.  Lingyang Chu Xia Hu Juhua Hu Lanjun Wang and Jian Pei. 2018. Exact and consistent interpretation for piecewise linear neural networks: A closed form solution. In ACM SIGKDD\/KDD. ACM 1244--1253."},{"key":"e_1_3_2_2_5_1","volume-title":"Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems. In 2016 IEEE SP","author":"Datta Anupam","unstructured":"Anupam Datta , Shayak Sen , and Yair Zick . 2016. Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems. In 2016 IEEE SP . IEEE , 598--617. Anupam Datta, Shayak Sen, and Yair Zick. 2016. Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems. In 2016 IEEE SP. IEEE, 598--617."},{"key":"e_1_3_2_2_6_1","unstructured":"Matthew F Dixon Nicholas G Polson and Vadim O Sokolov. [n. d.]. Deep learning for spatio-temporal modeling: Dynamic traffic flows and high frequency trading. Applied Stochastic Models in Business and Industry ([n. d.]).  Matthew F Dixon Nicholas G Polson and Vadim O Sokolov. [n. d.]. Deep learning for spatio-temporal modeling: Dynamic traffic flows and high frequency trading. Applied Stochastic Models in Business and Industry ([n. d.])."},{"key":"e_1_3_2_2_7_1","unstructured":"Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository.  Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository."},{"key":"e_1_3_2_2_8_1","unstructured":"Usama Fayyad and Keki Irani. 1993. Multi-interval discretization of continuous-valued attributes for classification learning. (1993).  Usama Fayyad and Keki Irani. 1993. Multi-interval discretization of continuous-valued attributes for classification learning. (1993)."},{"key":"e_1_3_2_2_9_1","volume-title":"d.]. Deep learning with long short-term memory networks for financial market predictions. EJOR","author":"Fischer Thomas","unstructured":"Thomas Fischer and Christopher Krauss . [n. d.]. Deep learning with long short-term memory networks for financial market predictions. EJOR , Vol. 270 ([n. d.]). Thomas Fischer and Christopher Krauss. [n. d.]. Deep learning with long short-term memory networks for financial market predictions. EJOR, Vol. 270 ([n. d.])."},{"key":"e_1_3_2_2_10_1","volume-title":"Numerical recipes in C: The art of scientific computing","author":"Flannery Brian P","unstructured":"Brian P Flannery , Saul A Teukolsky , William H Press , and William T Vetterling . 1988. Numerical recipes in C: The art of scientific computing . Vol. 2 . Brian P Flannery, Saul A Teukolsky, William H Press, and William T Vetterling. 1988. Numerical recipes in C: The art of scientific computing. Vol. 2."},{"key":"e_1_3_2_2_11_1","unstructured":"Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In AISTATS. 249--256.  Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In AISTATS. 249--256."},{"key":"e_1_3_2_2_12_1","volume-title":"Visualizing and understanding recurrent networks. arXiv preprint arXiv:1506.02078","author":"Karpathy Andrej","year":"2015","unstructured":"Andrej Karpathy , Justin Johnson , and Li Fei-Fei . 2015. Visualizing and understanding recurrent networks. arXiv preprint arXiv:1506.02078 ( 2015 ). Andrej Karpathy, Justin Johnson, and Li Fei-Fei. 2015. Visualizing and understanding recurrent networks. arXiv preprint arXiv:1506.02078 (2015)."},{"key":"e_1_3_2_2_13_1","volume-title":"Scalable greedy feature selection via weak submodularity. arXiv preprint arXiv:1703.02723","author":"Khanna Rajiv","year":"2017","unstructured":"Rajiv Khanna , Ethan Elenberg , Alexandros G Dimakis , Sahand Negahban , and Joydeep Ghosh . 2017. Scalable greedy feature selection via weak submodularity. arXiv preprint arXiv:1703.02723 ( 2017 ). Rajiv Khanna, Ethan Elenberg, Alexandros G Dimakis, Sahand Negahban, and Joydeep Ghosh. 2017. Scalable greedy feature selection via weak submodularity. arXiv preprint arXiv:1703.02723 (2017)."},{"key":"e_1_3_2_2_14_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1218772110"},{"key":"e_1_3_2_2_16_1","unstructured":"David Lewis. 2013. Counterfactuals .John Wiley & Sons.  David Lewis. 2013. Counterfactuals .John Wiley & Sons."},{"key":"e_1_3_2_2_17_1","volume-title":"Visualizing and understanding neural models in nlp. arXiv preprint arXiv:1506.01066","author":"Li Jiwei","year":"2015","unstructured":"Jiwei Li , Xinlei Chen , Eduard Hovy , and Dan Jurafsky . 2015. Visualizing and understanding neural models in nlp. arXiv preprint arXiv:1506.01066 ( 2015 ). Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. 2015. Visualizing and understanding neural models in nlp. arXiv preprint arXiv:1506.01066 (2015)."},{"key":"e_1_3_2_2_18_1","unstructured":"Zachary C Lipton. [n. d.]. The mythos of model interpretability. Queue ([n. d.]).  Zachary C Lipton. [n. d.]. The mythos of model interpretability. Queue ([n. d.])."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"crossref","unstructured":"Samaneh Mahdavifar and Ali A Ghorbani. 2019. Application of deep learning to cybersecurity: A survey. (2019).  Samaneh Mahdavifar and Ali A Ghorbani. 2019. Application of deep learning to cybersecurity: A survey. (2019).","DOI":"10.1016\/j.neucom.2019.02.056"},{"key":"e_1_3_2_2_20_1","unstructured":"Xudong Mao Qing Li Haoran Xie Raymond YK Lau Zhen Wang and Stephen Paul Smolley. [n. d.]. Least squares generative adversarial networks. In CVPR.  Xudong Mao Qing Li Haoran Xie Raymond YK Lau Zhen Wang and Stephen Paul Smolley. [n. d.]. Least squares generative adversarial networks. In CVPR."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.14778\/2733004.2733070"},{"key":"e_1_3_2_2_23_1","volume-title":"Deepfool: a simple and accurate method to fool deep neural networks","author":"Moosavi-Dezfooli Seyed-Mohsen","unstructured":"Seyed-Mohsen Moosavi-Dezfooli , Alhussein Fawzi , and Pascal Frossard . 2016. Deepfool: a simple and accurate method to fool deep neural networks . In IEEE CVPR. 2574--2582. Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. 2016. Deepfool: a simple and accurate method to fool deep neural networks. In IEEE CVPR. 2574--2582."},{"key":"e_1_3_2_2_24_1","volume-title":"Rise: Randomized input sampling for explanation of black-box models. In BMVC.","author":"Petsiuk Vitali","year":"2018","unstructured":"Vitali Petsiuk , Abir Das , and Kate Saenko . 2018 . Rise: Randomized input sampling for explanation of black-box models. In BMVC. Vitali Petsiuk, Abir Das, and Kate Saenko. 2018. Rise: Randomized input sampling for explanation of black-box models. In BMVC."},{"key":"e_1_3_2_2_25_1","volume-title":"Fani Deligianni, Melissa Berthelot, Javier Andreu-Perez, Benny Lo, and Guang-Zhong Yang.","author":"Rav Daniele","year":"2016","unstructured":"Daniele Rav ` i, Charence Wong , Fani Deligianni, Melissa Berthelot, Javier Andreu-Perez, Benny Lo, and Guang-Zhong Yang. 2016 . Deep learning for health informatics. IEEE journal of biomedical and health informatics, Vol. 21 , 1 (2016), 4--21. Daniele Rav`i, Charence Wong, Fani Deligianni, Melissa Berthelot, Javier Andreu-Perez, Benny Lo, and Guang-Zhong Yang. 2016. Deep learning for health informatics. IEEE journal of biomedical and health informatics, Vol. 21, 1 (2016), 4--21."},{"key":"e_1_3_2_2_26_1","unstructured":"Marco Tulio Ribeiro Sameer Singh and Carlos Guestrin. [n. d.]. \"Why Should I Trust You?\": Explaining the Predictions of Any Classifier. In KDD.  Marco Tulio Ribeiro Sameer Singh and Carlos Guestrin. [n. d.]. \"Why Should I Trust You?\": Explaining the Predictions of Any Classifier. In KDD."},{"key":"e_1_3_2_2_27_1","unstructured":"Sudeepa Roy and Dan Suciu. 2014. A formal approach to finding explanations for database queries. In ACM SIGMOD. ACM 1579--1590.  Sudeepa Roy and Dan Suciu. 2014. A formal approach to finding explanations for database queries. In ACM SIGMOD. ACM 1579--1590."},{"key":"e_1_3_2_2_28_1","unstructured":"Ira Shavitt and Eran Segal. 2018. Regularization learning networks: deep learning for tabular datasets. In NIPS. 1379--1389.  Ira Shavitt and Eran Segal. 2018. Regularization learning networks: deep learning for tabular datasets. In NIPS. 1379--1389."},{"key":"e_1_3_2_2_29_1","unstructured":"Craig Silverstein Sergey Brin Rajeev Motwani and Jeff Ullman. 2000. Scalable techniques for mining causal structures. (2000).  Craig Silverstein Sergey Brin Rajeev Motwani and Jeff Ullman. 2000. Scalable techniques for mining causal structures. (2000)."},{"key":"e_1_3_2_2_30_1","volume-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034","author":"Simonyan Karen","year":"2013","unstructured":"Karen Simonyan , Andrea Vedaldi , and Andrew Zisserman . 2013. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 ( 2013 ). Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)."},{"key":"e_1_3_2_2_31_1","volume-title":"Contrastive explanations with local foil trees. arXiv preprint arXiv:1806.07470","author":"van der Waa Jasper","year":"2018","unstructured":"Jasper van der Waa , Marcel Robeer , Jurriaan van Diggelen , Matthieu Brinkhuis , and Mark Neerincx . 2018. Contrastive explanations with local foil trees. arXiv preprint arXiv:1806.07470 ( 2018 ). Jasper van der Waa, Marcel Robeer, Jurriaan van Diggelen, Matthieu Brinkhuis, and Mark Neerincx. 2018. Contrastive explanations with local foil trees. arXiv preprint arXiv:1806.07470 (2018)."},{"key":"e_1_3_2_2_32_1","first-page":"841","article-title":"Counterfactual explanations without opening the black box: Automated decisions and the GDPR","volume":"31","author":"Wachter Sandra","year":"2017","unstructured":"Sandra Wachter , Brent Mittelstadt , and Chris Russell . 2017 . Counterfactual explanations without opening the black box: Automated decisions and the GDPR . Harv. JL & Tech. , Vol. 31 (2017), 841 . Sandra Wachter, Brent Mittelstadt, and Chris Russell. 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_2_33_1","doi-asserted-by":"publisher","DOI":"10.14778\/2536354.2536356"},{"key":"e_1_3_2_2_34_1","unstructured":"Xiaojun Xu Chang Liu Qian Feng Heng Yin Le Song and Dawn Song. 2017. Neural network-based graph embedding for cross-platform binary code similarity detection. In ACM SIGSAC CCS. 363--376.  Xiaojun Xu Chang Liu Qian Feng Heng Yin Le Song and Dawn Song. 2017. Neural network-based graph embedding for cross-platform binary code similarity detection. In ACM SIGSAC CCS. 363--376."},{"key":"e_1_3_2_2_35_1","unstructured":"Lei Yu and Huan Liu. 2003. Feature selection for high-dimensional data: A fast correlation-based filter solution. In ICML. 856--863.  Lei Yu and Huan Liu. 2003. Feature selection for high-dimensional data: A fast correlation-based filter solution. In ICML. 856--863."},{"key":"e_1_3_2_2_36_1","volume-title":"Visualizing and understanding convolutional networks","author":"Zeiler Matthew D","unstructured":"Matthew D Zeiler and Rob Fergus . 2014. Visualizing and understanding convolutional networks . In ECCV. Springer , 818--833. Matthew D Zeiler and Rob Fergus. 2014. Visualizing and understanding convolutional networks. In ECCV. Springer, 818--833."},{"key":"e_1_3_2_2_37_1","unstructured":"Xin Zhang Armando Solar-Lezama and Rishabh Singh. 2018. Interpreting neural network judgments via minimal stable and symbolic corrections. In NIPS.  Xin Zhang Armando Solar-Lezama and Rishabh Singh. 2018. Interpreting neural network judgments via minimal stable and symbolic corrections. In NIPS."},{"key":"e_1_3_2_2_38_1","unstructured":"Daniel John Zizzo Daniel Sgroi etal 2000. Bounded-rational behavior by neural networks in normal form games .Nuffield College.  Daniel John Zizzo Daniel Sgroi et al. 2000. Bounded-rational behavior by neural networks in normal form games .Nuffield College."}],"event":{"name":"KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event CA USA","acronym":"KDD '20","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 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403066","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394486.3403066","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394486.3403066","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:41:38Z","timestamp":1750200098000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403066"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,20]]},"references-count":37,"alternative-id":["10.1145\/3394486.3403066","10.1145\/3394486"],"URL":"https:\/\/doi.org\/10.1145\/3394486.3403066","relation":{},"subject":[],"published":{"date-parts":[[2020,8,20]]},"assertion":[{"value":"2020-08-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}