Convex Adversarial Collective Classification

By MohamadAli Torkamani, Daniel Lowd. 2013

42 citations recorded by the source corpus.

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Citations in the corpus

Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners (2015, background): “There is a related line of research on robust learning (Globerson and Roweis 2006; Torkamani and Lowd 2013; El Ghaoui et al. 2003; Xu, Caramanis, and Mannor 2009; Kim, Magnani, and Boyd 2005; Dekel, Shamir, and Xiao 2010).”
Attacking Graph-based Classification via Manipulating the Graph Structure (2019, background): “However, existing studies [7,8,12,18,19,21,27,30, 31,37,38,40,43,46,49,51–53,56,60,61,67,68,70] on adversarial machine learning mainly focused on machine learning for non-graph data, with only a few studies [15,16,59,74] as exceptions.”
Robustness May Be at Odds with Accuracy (2018, background): “A key implication of this view is that adversarial training should be beneﬁcial for the standard accuracy of a model (Torkamani & Lowd, 2013, 2014; Goodfellow et al., 2014b; Miyato et al., 2018).”
Adversarial Attacks on Neural Networks for Graph Data (2018, background): “Similarly, to improve robustness of collective classification via associative Markov networks, the work [32] considers adversarial noise in the features.”
Adversarial Attacks on Graph Neural Networks via Meta Learning (2019, background): “Torkamani & Lowd (2013) consider adversarial noise in the node features in order to improve robustness of collective classiﬁcation via associative Markov networks.”
Scalable Optimization of Randomized Operational Decisions in Adversarial Classification Settings (2015, methodology): “Extensive experiments demonstrate the efficacy of our approach.”
The Security of Latent Dirichlet Allocation (2015, background): “Robust learning designs desensitized models that are robust under attacks in a minimax sense (e.g. (Globerson & Roweis 2006, Torkamani & Lowd 2013, Barreno et al. 2010, Laskov & Lippmann 2010)).”
On Robustness and Regularization of Structural Support Vector Machines (2014, background): “Torkamani and Lowd (2013), show that for associative Markov networks, robust weight learning for collective classification can be efficiently done with a convex quadratic program.”
Pattern Recognition Systems under Attack: Design Issues and Research Challenges (2014, background): “Torkamani and Lowd [69] have faced the problem of collective classification in adversarial settings, where the goal is to jointly label a set of interconnected samples (e.”