By combining robust regression and prior information, we develop an effective robust regression method that can resist adaptive adversarial attacks. Due to the widespread existence of noise and data corruption, it is necessary to recover the true regression parameters when a certain proportion of the response variables have been corrupted. Methods to overcome this problem often involve robust least-squaresregression.

It has achieved great success on different vision-language downstream tasks suchasImage-TextRetrieval,Image-Captioning,andVisualQuestionAnswering.

In this setting, minibatch gradient estimates are exact (namely, exactly 0) on the set of global minimizers since data can be interpolated exactly.

Analysis of generalization properties of the proposed framework is given. An iterative Operator based Minimum Residual (OpMINRES) algorithm is proposed for solving the loss stabilization problem.

Earlier schemes could only handle stochastic substitutions anddeletions, andthus weareaiming foramore natural and appealing robustness guarantee that holds with respect to editdistance.