Section 4.18.0 - Introductory Writeups
Section 4.18.0.1 - Basic Writeup - Wikipedia - Regularizatin (Statistics)
Section 4.18.0.2 - Detailed Writeup - Bauer,Pereverzev,Rosasco - On Regularization Algorithms in Learning Theory
Section 4.18.1 - Bayesian Interpretation of Regularization
Section 4.18.2 - Regularization by Spectral Filtering
Section 4.18.3 - Matrix Regularization
Section 4.18.4 - Elastic Net Regularization
Section 4.18.5 - Total Variation(Total Variation Denoising) Regularization
Section 4.18.6 - Generalization
Section 4.18.7 - Tikhonov (Ridge, Weight Decay, Linear, Constrained Linear Inversion, Tikhonov-Miller, Phillips-Twomey) regularization
Section 4.18.7.0 - Introductory Writeup
Section 4.18.7.0.1 - Basic Writeup - Wikipedia - Tikhonov Regularization
Section 4.18.7.0.2 - Detailed Writeup - Wieringen - Lecture notes on ridge regression
Section 4.18.7.1 - Tikhonov-regularized least squares
Section 4.18.7.2 - Generalized Tikhonov regularization
Section 4.18.7.3 - Lavrentyev regularization
Section 4.18.7.4 - Regularization in Hilbert space
Section 4.18.7.4.1 - Detailed Writeups
Section 4.18.7.4.1.1 - Part 1 - Rosasco - Reproducing Kernel Hilbert Spaces
Section 4.18.7.4.1.2 - Part 2 - Dinuzzo,Scholkopf - The representer theorem for Hilbert spaces: a necessary and sufficient condition
Section 4.18.7.5 - Relation to singular-value decomposition and Wiener filter
Section 4.18.7.6 - Relation to probabilistic formulation
Section 4.18.7.7 - Bayesian interpretation
Section 4.18.7.7.1 - Detailed Writeup - Frogner - Bayesian Interpretation of Regularization
Section 4.18.7.7.2 - Bayesian interpretation of Kernel Regularization
Section 4.18.8 - Early stopping
Section 4.18.8.0 - Introductory Writeups
Section 4.18.8.0.1 - Basic Writeup - Wikipedia - Early Stopping
Section 4.18.8.0.2 - Detailed Writeups
Section 4.18.8.0.2.1 - Part 1 - Prechelt - Early Stopping - But When?
Section 4.18.8.0.2.2 - Part 2 - Prechelt - Automatic Early Stopping Using Cross Validation: Quantifying the Criteria
Section 4.18.9 - Regularizers for sparsity
Section 4.18.9.0 - Basic Writeup - Wikipedia - Regularization: Regularizers for Sparsity
Section 4.18.9.1 - Proximal methods
Section 4.18.9.1.0 - Basic Writeup - Wikipedia - Proximal Gradient Method
Section 4.18.9.1.1 - Projected Landweber
Section 4.18.9.1.2 - Alternating projection
Section 4.18.9.1.3 - Alternating-direction method of multipliers
Section 4.18.9.1.4 - Fast Iterative Shrinkage Thresholding Algorithm (FISTA)
Section 4.18.9.2 - Group sparsity without overlaps
Section 4.18.9.3 - Group sparsity with overlaps
Section 4.18.9.4 - Structured Sparsity
Section 4.18.10 - Regularizers for semi-supervised learning
Section 4.18.10.0 - Introductory Writeups
Section 4.18.10.0.1 - Basic Writeup - Wikipedia - Regularization: Regularizers for Semi-Supervised Learning
Section 4.18.10.0.2 - Detailed Writeups
Section 4.18.10.0.2.1 - Part 1 - Chen,Wang - Regularized Boos for Semi-Supervised Learning
Section 4.18.10.0.2.2 - Part 2 - Sindhwani,Niyogi,Belki - A Co-Regularization Approach to Semi-Supervised Learning with Multiple Views
Section 4.18.10.0.2.3 - Part 3 - Li,Zemel - High Order Regularization for Semi-Supervised Learning of Structured Output Problems
Section 4.18.10.0.2.4 - Part 4 - Belkin,Matveeva,Niyogi - Regularization and Semi-supervised Learning on Large Graphs
Section 4.18.11 - Regularizers for multitask learning
Section 4.18.11.0 - Basic Writeup - Wikipedia - Regularization: Regularizers for Multi_Task Learning
Section 4.18.11.1 - Sparse regularizer on columns
Section 4.18.11.2 - Nuclear norm regularization
Section 4.18.11.3 - Mean-constrained regularization
Section 4.18.11.4 - Clustered mean-constrained regularization
Section 4.18.11.5 - Graph-based similarity
Section 4.18.11.5.0 - Introductory Writeups
Section 4.18.11.5.0.1 - Basic Writeup - Wikipedia - Regularization: Graph Based Similarity
Section 4.18.11.5.0.2 - Detailed Writeup - Li,Mark,Raskutti,Willet - Graph-based Regularization for Regression Problems with Highly-correlated Designs
