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AI & MLRecommendedAIML-SEC-002

Adversarial Training and Certified Robustness

Training on worst-case perturbations, and proving robustness within a radius.

Difficulty
3 / 5
Importance
4 / 5
Study time
1–2 h
Kind of work
Mixed

Why it matters

The learner can explain adversarial training as a min-max problem and describe the robustness-accuracy trade-off with the perturbation budget.

The route to this topic

Route AIML-SEC-002
What Is a Model? Learning from Data Features and Targets Datasets and Splits Linear Regression I: Prediction and Residuals Sampling and Splits Train, Validation, and Test Errors Loss and Fitting: Squared Error Distribution Shift Offline Evaluation Objective Functions and Empirical Risk Robustness Evaluation Gradient Descent in Machine Learning Adversarial Examples Stochastic and Mini-batch Optimization Adversarial Training and Certified Robustness