empirical risk minimization

Secure Approximation Guarantee for Cryptographically Private Empirical Risk Minimization ACML 2016 Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences NeurIPS 2016 Stochastic Three-Composite Convex Minimization NeurIPS 2016 Stratified Sampling Meets Machine Learning ICML 2016 Variance Reduction for Faster Non-Convex Optimization ICML 2016 A Dual Augmented Block Minimization Framework for Learning with Limited Memory NeurIPS 2015 Adversarial Prediction Games for Multivariate Losses NeurIPS 2015 Competing with the Empirical Risk Minimizer in a Single Pass COLT 2015 Convex Risk Minimization and Conditional Probability Estimation COLT 2015 DiSCO: Distributed Optimization for Self-Concordant Empirical Loss ICML 2015 Efficient and Parsimonious Agnostic Active Learning NeurIPS 2015 Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction NeurIPS 2015 Fast Rates for Exp-concave Empirical Risk Minimization NeurIPS 2015 Learning and inference in the presence of corrupted inputs COLT 2015 Learning Sparse Low-Threshold Linear Classifiers JMLR 2015 Learning with Square Loss: Localization through Offset Rademacher Complexity COLT 2015 Local Smoothness in Variance Reduced Optimization NeurIPS 2015 Multiclass Learnability and the ERM Principle JMLR 2015 On Elicitation Complexity NeurIPS 2015 PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent ICML 2015 Proximal Average Approximated Incremental Gradient Method for Composite Penalty Regularized Empirical Risk Minimization ACML 2015 SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk NeurIPS 2015 Stochastic Dual Coordinate Ascent with Adaptive Probabilities ICML 2015 Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization ICML 2015 Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization ICML 2015