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On the Adversarial Robustness of LASSO Based Feature Selection
We formulate the modification strategy of the adversary as a bi-level optimization problem.
Optimal Feature Manipulation Attacks Against Linear Regression
In this paper, we investigate how to manipulate the coefficients obtained via linear regression by adding carefully designed poisoning data points to the dataset or modify the original data points.
On the Adversarial Robustness of Subspace Learning
We first characterize the optimal rank-one attack strategy that maximizes the subspace distance between the subspace learned from the original data matrix and that learned from the modified data matrix.
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