empirical risk minimization

The Fast Convergence of Boosting NeurIPS 2011 Empirical Bernstein Boosting AISTATS 2010 Empirical Risk Minimization with Approximations of Probabilistic Grammars NeurIPS 2010 Learnability, Stability and Uniform Convergence JMLR 2010 On the Theory of Learnining with Privileged Information NeurIPS 2010 Sample Complexity of Testing the Manifold Hypothesis NeurIPS 2010 Smoothness, Low Noise and Fast Rates NeurIPS 2010 Compressed Least-Squares Regression NeurIPS 2009 Efficient Learning using Forward-Backward Splitting NeurIPS 2009 Fast Learning from Non-i.i.d. Observations NeurIPS 2009 Lattice Regression NeurIPS 2009 Non-parametric Regression Between Manifolds NeurIPS 2008 On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization NeurIPS 2008 Overlaying classifiers: a practical approach for optimal ranking NeurIPS 2008 Learning Bounds for Domain Adaptation NeurIPS 2007 Structured Learning with Approximate Inference NeurIPS 2007 Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss JMLR 2006 Learning Minimum Volume Sets JMLR 2006 Stability of K-Means Clustering NeurIPS 2006 Stability Properties of Empirical Risk Minimization over Donsker Classes JMLR 2006 Training Conditional Random Fields for Maximum Labelwise Accuracy NeurIPS 2006 Asymptotics in Empirical Risk Minimization JMLR 2005 Machine Learning with Data Dependent Hypothesis Classes JMLR 2002 Recommender Systems Using Linear Classifiers JMLR 2002