Nash Equilibria of Static Prediction Games

By Michael Brückner, Tobias Scheffer. 2009

91 citations recorded by the source corpus.

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In Game & Decision Theory

Citations in the corpus

Poisoning Attacks against Support Vector Machines (2012, methodology): “Compared to previous work on evasion of learning algorithms ( e.g. , Br¨uckner & Scheﬀer, 2009; Kloft & Laskov, 2010), such inﬂuence may facilitate the practical realization of various evasion strategies.”
Query Strategies for Evading Convex-Inducing Classifiers (2010, background): “Thus, binary search on the exponent achieves ǫ -multiplicative optimality and does so with the fewest queries.”
Data Poisoning Attacks against Autoregressive Models (2016, background): “A somewhat orthogonal line of research has posed the problem of learning in the presence of adversaries in a game theoretic context (Liu and Chawla 2009; Br¨uckner, Kanzow, and Scheffer 2012; Dalvi et al. 2004; Br¨uckner and Scheffer 2009; 2011).”
Convex Adversarial Collective Classification (2013, methodology): “More recently, Brückner and Scheffer showed that, under modest assumptions, Nash equilibria can be found for domains such as spam (Brückner & Scheffer, 2009).”
Static prediction games for adversarial learning problems (2012, methodology): “1 Existence of a Nash Equilibrium Theorem 1 of Brückner and Scheffer (2009) identifies conditions under which a unique Nash equilibrium exists.”
Scalable Optimization of Randomized Operational Decisions in Adversarial Classification Settings (2015, methodology): “This decision (and the choice of the threshold) are often motivated by overall tolerance for false positives, as well as operational considerations, for example, to ensure that the number of alerts does not exceed what can reasonably be inspected by security professionals.”
Strategic Classification (2015, background): “More recent work considers alternative game-theoretic notions [BS09, BS11, BKS12, GSBS13].”
Explicit Defense Actions Against Test-Set Attacks (2017, background): “A separate line of research has posed the problem of learning in the presence of adversaries in game theoretic contexts (Liu and Chawla 2009; Brückner, Kanzow, and Scheffer 2012; Dalvi et al. 2004; Brückner and Scheffer 2009; 2011; Hardt et al. 2016; Brückner and Scheffer 2011).”
Adversarial machine learning (2019, background): “Subsequent game theoretic approaches to learning have extended this setting and solved for equilibria of the game [8, 32].”
Pattern Recognition Systems under Attack: Design Issues and Research Challenges (2014, background): “[31]; then, more rigorous approaches have introduced Nash and Stackelberg games for secure learning, deriving formal conditions for the corresponding equilibrium strategies [25, 45, 49, 26, 24].”
Stackelberg games for adversarial prediction problems (2011, methodology): “We compare a regular support vector machine (SVM), logistic regression (LogReg), the SVM for invariances with feature scaling (Invar-SVM, [14]), Nash logistic regression (Nash, [1]), and the Stackelberg instances SPG with worst-case loss (SPG, cf.”
Optimal randomized classification in adversarial settings (2014, background): “, [8, 5]), and studies of the algorithmic complexity of the adversary’s classifier evasion problem (e.”