Hi, I am happy to announce three packages related to empirical risk minimization
EmpiricalRisks <https://github.com/lindahua/EmpiricalRisks.jl> This Julia package provides a collection of predictors and loss functions, as well as the efficient computation of gradients, mainly to support the implementation of (regularized) empirical risk minimization methods. Predictors: - linear prediction - affine prediction - multivariate linear prediction - multivariate affine prediction Loss functions: - squared loss - absolute loss - quantile loss - huber loss - hinge loss - smoothed hinge loss - logistic loss - sum squared loss (for multivariate prediction) - multinomial logistic loss Regularizers: - squared L2 regularization - L1 regularization - elastic net (L1 + squared L2) - evaluation of proximal operators, w.r.t. these regularizers. Regression <https://github.com/lindahua/Regression.jl> This package was dead before, and I revived it recently. It is based on EmpiricalRisks, and provides methods for regression analysis (for moderate size problems, i.e. the data can be loaded entirely to memory). It supports the following problems: - Linear regression - Ridge regression - LASSO - Logistic regression - Multinomial Logistic regression - Problems with customized loss and regularizers It also provides a variety of solvers: - Analytical solution (for linear & ridge regression) - Gradient descent - BFGS - L-BFGS - Proximal gradient descent (recommended for LASSO & sparse regression) - Accelerated gradient descent (experimental) SGDOptim <https://github.com/lindahua/SGDOptim.jl> I announced this couple weeks ago. Now this package has been fundamentally refactored, and now it is based on EmpiricalRisks. It aims to provide stochastic algorithms (e.g. SGD) for solve large scale regression problems. Cheers, Dahua
