stochastic optimization

Finite-sum Composition Optimization via Variance Reduced Gradient Descent AISTATS 2017 First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization NeurIPS 2017 Follow the Moving Leader in Deep Learning ICML 2017 Generalization for Adaptively-chosen Estimators via Stable Median COLT 2017 Gradient Boosting on Stochastic Data Streams AISTATS 2017 Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks NeurIPS 2017 Learning RBM with a DC programming Approach ACML 2017 Less than a Single Pass: Stochastically Controlled Stochastic Gradient AISTATS 2017 Linear Convergence of Stochastic Frank Wolfe Variants AISTATS 2017 Linear Thompson Sampling Revisited AISTATS 2017 Monte-Carlo Tree Search by Best Arm Identification NeurIPS 2017 Natasha: Faster Non-Convex Stochastic Optimization via Strongly Non-Convex Parameter ICML 2017 Nonlinear Acceleration of Stochastic Algorithms NeurIPS 2017 On Perturbed Proximal Gradient Algorithms JMLR 2017 Optimistic Planning for the Stochastic Knapsack Problem AISTATS 2017 Probabilistic Line Searches for Stochastic Optimization JMLR 2017 Reducing Reparameterization Gradient Variance NeurIPS 2017 Robust Guarantees of Stochastic Greedy Algorithms ICML 2017 Robust Optimization for Non-Convex Objectives NeurIPS 2017 Second-Order Stochastic Optimization for Machine Learning in Linear Time JMLR 2017 Sparse Accelerated Exponential Weights AISTATS 2017 Stochastic Adaptive Quasi-Newton Methods for Minimizing Expected Values ICML 2017 Stochastic and Adversarial Online Learning without Hyperparameters NeurIPS 2017 Stochastic Composite Least-Squares Regression with Convergence Rate $O(1/n)$ COLT 2017 Stochastic Constraint Programming IJCAI 2017