This Julia package provides a collection of predictors and loss functions, mainly to support the implementation of (regularized) empirical risk minimization methods.
Currently, the following higherlevel packages are depending on EmpiricalRisks:
 Regression: solving moderatesize problem using conventional optimization techniques.
 SGDOptim: solving largescale problem using stochastic gradient descent or its variants.
This package provides basic components for implementing regularized empirical risk minimization:

Prediction models
u = f(x; θ)
 linear prediction
 affine prediction
 multivariate linear prediction
 multivariate affine prediction

Loss functions
loss(u, y)
 squared loss
 absolute loss
 quantile loss
 huber loss
 hinge loss
 squared hinge loss
 smoothed hinge loss
 logistic loss
 sum squared loss (for multivariate prediction)
 multinomial logistic loss
Notes:
 For each loss function, we provide methods to compute the loss value, the derivative/gradient, or both (at the same time).
 For each (consistent) combination of loss function and prediction model (which together are referred to as a risk model), we provide methods to compute the total risk and the gradient w.r.t. the parameter.

Regularizers
 squared L2
 L1
 elastic net (L1 + squared L2)
Notes:
 For each regularizer, we provide methods to evaluate the regularization value, the gradient, and the proximal operator.
Remarks: All functions in this package are carefully optimized and tested.
Here is the Detailed Documentation.