Module regularization 

Multi-Task Regularization Methods

This module provides various regularization techniques specifically designed for multi-task and multi-output learning scenarios. These methods help in learning shared structure across tasks while preventing overfitting.

The module has been refactored into smaller submodules to comply with the 2000-line limit:

• simd_ops - SIMD-accelerated operations for high-performance regularization computations
• group_lasso - Group Lasso regularization for feature group selection
• nuclear_norm - Nuclear norm regularization for low-rank structure learning
• task_clustering - Task clustering regularization for similar task grouping
• task_relationship - Task relationship learning for explicit task relationships
• meta_learning - Meta-learning approach for quick adaptation to new tasks

Re-exports

pub use group_lasso::GroupLasso;

pub use group_lasso::GroupLassoTrained;

pub use meta_learning::MetaLearningMultiTask;

pub use meta_learning::MetaLearningMultiTaskTrained;

pub use nuclear_norm::NuclearNormRegression;

pub use nuclear_norm::NuclearNormRegressionTrained;

pub use task_clustering::TaskClusteringRegressionTrained;

pub use task_clustering::TaskClusteringRegularization;

pub use task_relationship::TaskRelationshipLearning;

pub use task_relationship::TaskRelationshipLearningTrained;

pub use task_relationship::TaskSimilarityMethod;

Modules

group_lasso

Group Lasso Regularization for Multi-Task Learning

meta_learning

Meta-Learning for Multi-Task Learning

nuclear_norm

Nuclear Norm Regularization for Multi-Task Learning

simd_ops

SIMD-accelerated operations for high-performance regularization computations

task_clustering

Task Clustering Regularization for Multi-Task Learning

task_relationship

Task Relationship Learning for Multi-Task Learning

Structs

MultiTaskElasticNet

Multi-Task Elastic Net with Group Structure

MultiTaskElasticNetTrained

Trained state for MultiTaskElasticNet

Enums

RegularizationStrategy

Regularization strategies for multi-task learning