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Many safety-critical applications of machine learning, such as fraud or abuse detection, use data in tabular domains.
A formal basis for the heuristic determination of minimum cost paths
Peter E. Hart, Nils J. Nilsson, and Bertram Raphael · 1968
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Heuristic search viewed as path finding in a graph
Ira Pohl · 1970
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A general coefficient of similarity and some of its properties
John C Gower · 1971
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Maximising real-valued submodular functions: Primal and dual heuristics for location problems
Laurence A. Wolsey · 1982
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Iterative-Deepening-A*: An optimal admissible tree search
Richard E. Korf · 1985
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Generalized best-first search strategies and the optimality of A*
Rina Dechter and Judea Pearl · 1985
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The budgeted maximum coverage problem
Samir Khuller, Anna Moss, and Joseph Naor · 1999
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Adversarial learning
Daniel Lowd and Christopher Meek · 2005
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Can machine learning be secure?
Marco Barreno, Blaine Nelson, Russell Sears, Anthony D. Joseph, and J. D. Tygar · 2006
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Adaptive algorithm for sparse system identification using projections onto weighted l1 balls
Konstantinos Slavakis, Yannis Kopsinis, and Sergios Theodoridis · 2010
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Potential search: A bounded-cost search algorithm
Roni Stern, Rami Puzis, and Ariel Felner · 2011
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2013
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Potential-based bounded-cost search and anytime non-parametric A*
Roni Stern, Ariel Felner, Jur van den Berg, Rami Puzis, Rajat Shah, and Ken Goldberg · 2014
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Deepfool: A simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Evasion and hardening of tree ensemble classifiers
Alex Kantchelian, J. D. Tygar, and Anthony D. Joseph · 2016
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Strategic classification
Moritz Hardt, Nimrod Megiddo, Christos H. Papadimitriou, and Mary Wootters · 2016
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Yes, machine learning can be more secure! A case study on android malware detection
Ambra Demontis, Marco Melis, Battista Biggio, Davide Maiorca, Daniel Arp, Konrad Rieck, Igino Corona, Giorgio Giacinto, and Fabio Roli · 2017
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On the (statistical) detection of adversarial examples
Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, and Patrick D. McDaniel · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David A. Wagner · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Classification of twitter accounts into automated agents and human users
Zafar Gilani, Ekaterina Kochmar, and Jon Crowcroft · 2017
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Adversarial malware binaries: Evading deep learning for malware detection in executables
Bojan Kolosnjaji, Ambra Demontis, Battista Biggio, Davide Maiorca, Giorgio Giacinto, Claudia Eckert, and Fabio Roli · 2018
Cited alongside, same era.
Attriguard: A practical defense against attribute inference attacks via adversarial machine learning
Jinyuan Jia and Neil Zhenqiang Gong · 2018
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Discrete adversarial attacks and submodular optimization with applications to text classification
Qi Lei, Lingfei Wu, Pin-Yu Chen, Alexandros G Dimakis, Inderjit S Dhillon, and Michael Witbrock · 2018
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Hotflip: White-box adversarial examples for text classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou · 2018
Cited alongside, same era.
Search-based adversarial testing and improvement of constrained credit scoring systems
Salah Ghamizi, Maxime Cordy, Martin Gubri, Mike Papadakis, Andrey Boytsov, Yves Le Traon, and Anne Goujon · 2020
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POTs: protective optimization technologies
Bogdan Kulynych, Rebekah Overdorf, Carmela Troncoso, and Seda F. Gürses · 2020
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Politics of adversarial machine learning
Kendra Albert, Jonathon Penney, Bruce Schneier, and Ram Shankar Siva Kumar · 2020
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Greedy attack and Gumbel attack: Generating adversarial examples for discrete data
Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, and Michael I Jordan · 2020
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Attackability characterization of adversarial evasion attack on discrete data
Yutong Wang, Yufei Han, Hongyan Bao, Yun Shen, Fenglong Ma, Jin Li, and Xiangliang Zhang · 2020
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Evasion attacks against banking fraud detection systems
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Deep text classification can be fooled
Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, and Wenchang Shi · 2018
Cited alongside, same era.
On the suitability of l p l_{p} -norms for creating and preventing adversarial examples
Mahmood Sharif, Lujo Bauer, and Michael K. Reiter · 2018
Cited alongside, same era.
Cost-sensitive robustness against adversarial examples
Xiao Zhang and David Evans · 2018
Cited alongside, same era.
Scaling provable adversarial defenses
Eric Wong, Frank Schmidt, Jan Hendrik Metzen, and J. Zico Kolter · 2018
Cited alongside, same era.
Wild patterns: Ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli · 2018
Cited alongside, same era.
Evading classifiers in discrete domains with provable optimality guarantees
Bogdan Kulynych, Jamie Hayes, Nikita Samarin, and Carmela Troncoso · 2018
Cited alongside, same era.
Strategic classification from revealed preferences
Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, and Zhiwei Steven Wu · 2018
Cited alongside, same era.
Michele Carminati, Luca Santini, Mario Polino, and Stefano Zanero · 2020
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Intriguing properties of adversarial ML attacks in the problem space
Fabio Pierazzi, Feargus Pendlebury, Jacopo Cortellazzi, and Lorenzo Cavallaro · 2020
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Fast is better than free: Revisiting adversarial training
Eric Wong, Leslie Rice, and J. Zico Kolter · 2020
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Efficient projection algorithms onto the weighted l1 ball
Guillaume Perez, Sebastian Ament, Carla Gomes, and Michel Barlaud · 2020
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Adversarial robustness against the union of multiple perturbation models
Pratyush Maini, Eric Wong, and Zico Kolter · 2020
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Not all datasets are born equal: On heterogeneous data and adversarial examples
Eden Levy, Yael Mathov, Ziv Katzir, Asaf Shabtai, and Yuval Elovici · 2020
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Treant: training evasion-aware decision trees
Stefano Calzavara, Claudio Lucchese, Gabriele Tolomei, Seyum Assefa Abebe, and Salvatore Orlando · 2020
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Perceptual adversarial robustness: Defense against unseen threat models
Cassidy Laidlaw, Sahil Singla, and Soheil Feizi · 2021
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On the effectiveness of adversarial training against common corruptions
Klim Kireev, Maksym Andriushchenko, and Nicolas Flammarion · 2021
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Tabnet: Attentive interpretable tabular learning
Sercan Ö Arik and Tomas Pfister · 2021
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Adversarial attacks for tabular data: Application to fraud detection and imbalanced data
Francesco Cartella, Orlando Anunciacao, Yuki Funabiki, Daisuke Yamaguchi, Toru Akishita, and Olivier Elshocht · 2021
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Cost-aware robust tree ensembles for security applications
Yizheng Chen, Shiqi Wang, Weifan Jiang, Asaf Cidon, and Suman Jana · 2021
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Efficient training of robust decision trees against adversarial examples
Daniël Vos and Sicco Verwer · 2021
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Pac-learning for strategic classification
Ravi Sundaram, Anil Vullikanti, Haifeng Xu, and Fan Yao · 2021
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Adversarial robustness for tabular data through cost and utility awareness
Klim Kireev, Bogdan Kulynych, and Carmela Troncoso · 2023
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