However, the pretrained model may not provide an ideal optimization starting point for the test-time optimization.

Likeintheclassical graphical lassoproblem, recent works made use of the`1-norm with the goal of promoting sparsity in the Laplacian constrained precision matrix estimation.

Theseestimators havethegreatadvantageof being calculated using only theobservedinformation, leveraging theunderlying low-rank structure of the data.

After that, the leaner solely and assiduously learns a target model from this dataset without further interaction.

Here, we examine several objective functions for adversarial attack construction proposed previously and present asolution toalleviate the effect ofthese attacks.