namkoong-lab / dro

What it does

Without specified, our DRO model is to solve the following optimization problem: $$\min_{\theta} \max_{P: P \in U} E_{(X,Y) \sim P}[\ell(\theta;(X, Y))],$$ where $U$ is the so-called ambiguity set and typically of the form $U = \\{P: d(P, \hat P) \leq \epsilon\\}$ and $\hat P := \frac{1}{n}\sum_{i = 1}^n \delta_{(X_i, Y_i)}$ is the empirical distribution of training samples $\{(X_i, Y_i)\}_{i = 1}^n$. And $\epsilon$ is the hyperparameter.