← Learning Types
Machine Learning › Learning Types ›

Regularization

97 directly classified papers

Papers per year

Papers

Altitude Training: Strong Bounds for Single-Layer Dropout NIPS 2014
On Robustness and Regularization of Structural Support Vector Machines ICML 2014
Structure Regularization for Structured Prediction NIPS 2014
Better Approximation and Faster Algorithm Using the Proximal Average NIPS 2013
Dropout Training as Adaptive Regularization NIPS 2013
Reconciling "priors" & "priors" without prejudice? NIPS 2013
Estimating LASSO Risk and Noise Level NIPS 2013
Understanding Dropout NIPS 2013
Adaptive dropout for training deep neural networks NIPS 2013
Calibrated Elastic Regularization in Matrix Completion NIPS 2012
Synchronization can Control Regularization in Neural Systems via Correlated Noise Processes NIPS 2012
Regularized Laplacian Estimation and Fast Eigenvector Approximation NIPS 2011
Shaping Level Sets with Submodular Functions NIPS 2011
Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net AISTATS 2010
Learning Multiple Tasks using Manifold Regularization NIPS 2010
Sufficient Conditions for Generating Group Level Sparsity in a Robust Minimax Framework NIPS 2010
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers NIPS 2009
Adaptive Regularization of Weight Vectors NIPS 2009
Regularized Learning with Networks of Features NIPS 2008
On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization NIPS 2008
A Spectral Regularization Framework for Multi-Task Structure Learning NIPS 2007
Bundle Methods for Machine Learning NIPS 2007