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.
L2 Loss
L2 loss is the squared-error loss function: for a true value…
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 $\hat{y}$