Distributionally Robust Optimization
Distributionally robust optimization (DRO) is a framework for making decisions when the probability distribution of uncertain inputs is not known exactly.
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L1 Loss
L1 loss is a regression loss function equal to the average of the absolute differences between predicted values and target values, written as $$\frac{1}{n} \sum \lvert y_i - \hat{y}_i \rvert$$.
L2 Loss
L2 loss is the squared-error loss function: for a true value $$y$$ and a predicted value $$\hat{y}$$, it is the squared difference $$(y - \hat{y})^2$$, and averaging it across a dataset gives the mean squared…
Ridge Regularization
Ridge regularization, also known as L2 regularization or Tikhonov regularization, is a technique in statistics and machine learning that adds a squared L2-norm penalty to a model's loss function.
Simulated annealing
Simulated annealing is a probabilistic method for finding a good approximate solution to a global optimization problem, especially one with many local optima that trap ordinary hill-climbing methods.
Squared Loss
Squared loss, also called quadratic loss, L2 loss, or squared error loss, is a loss function that penalizes a prediction by the square of its error: for a true value $y$ and a prediction $\hat{y}$