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Recent development in the field of explainable artificial intelligence (XAI) has helped improve trust in Machine-Learning-as-a-Service (MLaaS) systems, in which an explanation is provided together with the model prediction in response to each query.
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Heo, J.; Joo, S.; and Moon, T. 2019 · 2019
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Certified robustness to adversarial examples with differential privacy
Lecuyer, M.; Atlidakis, V.; Geambasu, R.; Hsu, D.; and Jana, S. 2019 · 2019
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Model reconstruction from model explanations
Milli, S.; Schmidt, L.; Dragan, A. D.; and Hardt, M. 2019 · 2019
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Heterogeneous Gaussian mechanism: preserving differential privacy in deep learning with provable robustness
Phan, N.; Vu, M. N.; Liu, Y.; Jin, R.; Dou, D.; Wu, X.; and Thai, M. T. 2019 · 2019
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Intrinsic certified robustness of bagging against data poisoning attacks
Jia, J.; Cao, X.; and Gong, N. Z. 2021 · 2021
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MEGEX: Data-Free Model Extraction Attack against Gradient-Based Explainable AI
Miura, T.; Hasegawa, S.; and Shibahara, T. 2021 · 2021
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Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers
Severi, G.; Meyer, J.; Coull, S.; and Oprea, A. 2021 · 2021
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Shokri, R.; Strobel, M.; and Zick, Y. 2021 · 2021
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Zhao, X.; Zhang, W.; Xiao, X.; and Lim, B. 2021 · 2021
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Explainable Machine Learning Challenge
Community, F. 2018 · 2022
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Boosting Randomized Smoothing with Variance Reduced Classifiers
Horváth, M.; Mueller, M.; Fischer, M.; and Vechev, M. 2022 · 2022
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NeuCEPT: Learn Neural Networks’s Mechanism via Critical Neurons with Precision Guarantee
Vu, M. N.; Nguyen, T.; and Thai, M. T. 2022 · 2022
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