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Imperceptible adversarial attacks on tabular data. arXiv ( 2019 ). Vincent Ballet, Xavier Renard, Jonathan Aigrain, Thibault Laugel, Pascal Frossard, and Marcin Detyniecki. 2019. Imperceptible adversarial attacks on tabular data. arXiv (2019)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Philipp Benz Chaoning Zhang Adil Karjauv and In So Kweon. 2021. Revisiting batch normalization for improving corruption robustness. In WACV.  Philipp Benz Chaoning Zhang Adil Karjauv and In So Kweon. 2021. Revisiting batch normalization for improving corruption robustness. In WACV.","DOI":"10.1109\/WACV48630.2021.00054"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"N. Carlini and D. Wagner. 2017. Towards Evaluating the Robustness of Neural Networks. In IEEE S&P.  N. Carlini and D. Wagner. 2017. Towards Evaluating the Robustness of Neural Networks. In IEEE S&P.","DOI":"10.1109\/SP.2017.49"},{"key":"e_1_3_2_1_5_1","volume-title":"Adversarial attacks for tabular data: Application to fraud detection and imbalanced data. SafeAI@AAAI","author":"Cartella Francesco","year":"2021","unstructured":"Francesco Cartella , Orlando Anunciacao , Yuki Funabiki , Daisuke Yamaguchi , Toru Akishita , and Olivier Elshocht . 2021. Adversarial attacks for tabular data: Application to fraud detection and imbalanced data. SafeAI@AAAI ( 2021 ). Francesco Cartella, Orlando Anunciacao, Yuki Funabiki, Daisuke Yamaguchi, Toru Akishita, and Olivier Elshocht. 2021. Adversarial attacks for tabular data: Application to fraud detection and imbalanced data. SafeAI@AAAI (2021)."},{"key":"e_1_3_2_1_6_1","volume-title":"EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples. In AAAI.","author":"Chen Pin-Yu","year":"2018","unstructured":"Pin-Yu Chen , Yash Sharma , Huan Zhang , Jinfeng Yi , and Cho-Jui Hsieh . 2018 . EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples. In AAAI. Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, and Cho-Jui Hsieh. 2018. EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples. In AAAI."},{"key":"e_1_3_2_1_7_1","unstructured":"Criteo. 2014. Kaggle Display Advertising Challenge Dataset. http:\/\/labs.criteo.com\/2014\/02\/kaggle-display-advertising-challenge-dataset\/  Criteo. 2014. Kaggle Display Advertising Challenge Dataset. http:\/\/labs.criteo.com\/2014\/02\/kaggle-display-advertising-challenge-dataset\/"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143874"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Yinpeng Dong Fangzhou Liao Tianyu Pang Hang Su Jun Zhu Xiaolin Hu and Jianguo Li. 2018. Boosting Adversarial Attacks with Momentum. In CVPR.  Yinpeng Dong Fangzhou Liao Tianyu Pang Hang Su Jun Zhu Xiaolin Hu and Jianguo Li. 2018. Boosting Adversarial Attacks with Momentum. In CVPR.","DOI":"10.1109\/CVPR.2018.00957"},{"key":"e_1_3_2_1_10_1","volume-title":"Adversarial robustness with non-uniform perturbations. NeurIPS","author":"Erdemir Ecenaz","year":"2021","unstructured":"Ecenaz Erdemir , Jeffrey Bickford , Luca Melis , and Sergul Aydore . 2021. Adversarial robustness with non-uniform perturbations. NeurIPS ( 2021 ). Ecenaz Erdemir, Jeffrey Bickford, Luca Melis, and Sergul Aydore. 2021. Adversarial robustness with non-uniform perturbations. NeurIPS (2021)."},{"key":"e_1_3_2_1_11_1","unstructured":"Ian Goodfellow Jonathon Shlens and Christian Szegedy. 2015. Explaining and Harnessing Adversarial Examples. In ICLR.  Ian Goodfellow Jonathon Shlens and Christian Szegedy. 2015. Explaining and Harnessing Adversarial Examples. In ICLR."},{"key":"e_1_3_2_1_12_1","volume-title":"Unering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples. arXiv","author":"Gowal Sven","year":"2020","unstructured":"Sven Gowal , Chongli Qin , Jonathan Uesato , Timothy Mann , and Pushmeet Kohli . 2020. Unering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples. arXiv ( 2020 ). Sven Gowal, Chongli Qin, Jonathan Uesato, Timothy Mann, and Pushmeet Kohli. 2020. Unering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples. arXiv (2020)."},{"key":"e_1_3_2_1_13_1","unstructured":"Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. IJCAI.  Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. IJCAI."},{"key":"e_1_3_2_1_14_1","volume-title":"The movielens datasets: History and context. 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Adam: A method for stochastic optimization. arXiv (2014)."},{"key":"e_1_3_2_1_18_1","unstructured":"Volodymyr Kuleshov Shantanu Thakoor Tingfung Lau and Stefano Ermon. 2018. Adversarial examples for natural language classification problems. (2018).  Volodymyr Kuleshov Shantanu Thakoor Tingfung Lau and Stefano Ermon. 2018. Adversarial examples for natural language classification problems. (2018)."},{"key":"e_1_3_2_1_19_1","volume-title":"Workshop track, ICLR.","author":"Kurakin Alexey","year":"2017","unstructured":"Alexey Kurakin , Ian J. Goodfellow , and Samy Bengio . 2017 . Adversarial examples in the physical world .. In Workshop track, ICLR. Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio. 2017. Adversarial examples in the physical world.. In Workshop track, ICLR."},{"key":"e_1_3_2_1_20_1","volume-title":"Discrete adversarial attacks and submodular optimization with applications to text classification. 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Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_22_1","unstructured":"Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras and Adrian Vladu. 2018. Towards Deep Learning Models Resistant to Adversarial Attacks. In ICLR.  Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras and Adrian Vladu. 2018. Towards Deep Learning Models Resistant to Adversarial Attacks. In ICLR."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108377"},{"key":"e_1_3_2_1_24_1","volume-title":"Adversarial training methods for semi-supervised text classification. ICLR","author":"Miyato Takeru","year":"2016","unstructured":"Takeru Miyato , Andrew M Dai , and Ian Goodfellow . 2016. Adversarial training methods for semi-supervised text classification. ICLR ( 2016 ). Takeru Miyato, Andrew M Dai, and Ian Goodfellow. 2016. Adversarial training methods for semi-supervised text classification. 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In ICLR. https:\/\/openreview.net\/forum?id=Xb8xvrtB8Ce  Tianyu Pang Xiao Yang Yinpeng Dong Hang Su and Jun Zhu. 2021. Bag of Tricks for Adversarial Training. In ICLR. https:\/\/openreview.net\/forum?id=Xb8xvrtB8Ce"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Nicolas Papernot Patrick McDaniel Ananthram Swami and Richard Harang. 2016. Crafting adversarial input sequences for recurrent neural networks. In MILCOM.  Nicolas Papernot Patrick McDaniel Ananthram Swami and Richard Harang. 2016. Crafting adversarial input sequences for recurrent neural networks. In MILCOM.","DOI":"10.1109\/MILCOM.2016.7795300"},{"key":"e_1_3_2_1_29_1","unstructured":"Martin Pawelczyk Chirag Agarwal Shalmali Joshi Sohini Upadhyay and Himabindu Lakkaraju. 2022. Exploring counterfactual explanations through the lens of adversarial examples: A theoretical and empirical analysis. In AIS-TATS.  Martin Pawelczyk Chirag Agarwal Shalmali Joshi Sohini Upadhyay and Himabindu Lakkaraju. 2022. 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Towards the first adversarially robust neural network model on MNIST. In ICLR.  Lukas Schott Jonas Rauber Matthias Bethge and Wieland Brendel. 2019. Towards the first adversarially robust neural network model on MNIST. In ICLR."},{"key":"e_1_3_2_1_33_1","volume-title":"Notes on the n-Person Game-II: The Value of an n-Person Game.(1951). Lloyd S Shapley","author":"Shapley Lloyd S","year":"1951","unstructured":"Lloyd S Shapley . 1951. Notes on the n-Person Game-II: The Value of an n-Person Game.(1951). Lloyd S Shapley ( 1951 ). Lloyd S Shapley. 1951. Notes on the n-Person Game-II: The Value of an n-Person Game.(1951). Lloyd S Shapley (1951)."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357925"},{"key":"e_1_3_2_1_35_1","unstructured":"Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna Dumitru Erhan Ian Goodfellow and Rob Fergus. 2014. Intriguing properties of neural networks. In ICLR.  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