Standard Regularizations

Classical regularization methods for parameter penalization and constraint enforcement. These regularizations add penalty terms to the loss function to encourage desired properties like sparsity (L1), smoothness (L2), or structural constraints (orthogonality, spectral norms). Use with Param to automatically include regularization losses in training objectives.

Regularization	Abstract base class for parameter regularization.
L1Reg	L1 (Lasso) regularization.
L2Reg	L2 (Ridge) regularization.
ElasticNetReg	Elastic Net regularization (combination of L1 and L2).
HuberReg	Huber regularization (robust regularization).
GroupLassoReg	Group Lasso regularization.
TotalVariationReg	Total Variation regularization.
MaxNormReg	Max Norm regularization (soft constraint).
EntropyReg	Entropy regularization.
OrthogonalReg	Orthogonal regularization.
SpectralNormReg	Spectral Norm regularization.
ChainedReg	Composite regularization that chains multiple regularizations together.

Prior Distribution-Based Regularizations

Probabilistic regularizations based on prior distributions for Bayesian-inspired parameter estimation. These regularizations encode domain knowledge or assumptions about parameter distributions (Gaussian, heavy-tailed, bounded, etc.). Particularly useful for variational inference, maximum a posteriori (MAP) estimation, and uncertainty quantification. Each regularization implements loss(), sample_init(), and reset_value() for prior-based parameter initialization.

GaussianReg	Gaussian prior regularization.
StudentTReg	Student's t-distribution prior regularization.
CauchyReg	Cauchy prior regularization.
UniformReg	Uniform prior regularization (soft bounded constraint).
BetaReg	Beta prior regularization (for parameters in [0, 1]).
LogNormalReg	Log-normal prior regularization (for positive parameters).
ExponentialReg	Exponential prior regularization (for positive parameters).
GammaReg	Gamma prior regularization (for positive parameters).
InverseGammaReg	Inverse-Gamma prior regularization (for variance parameters).
LogUniformReg	Log-uniform (Jeffreys) prior regularization (scale-invariant).
HorseshoeReg	Horseshoe prior regularization (strong sparsity with heavy tails).
SpikeAndSlabReg	Spike-and-slab prior regularization (variable selection).
DirichletReg	Dirichlet prior regularization (for probability simplexes).