Stochastic Methods

SADAGRAD: Strongly Adaptive Stochastic Gradient Methods ICML 2018 Sampling Informative Training Data for RNN Language Models ACL 2018 Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation NeurIPS 2018 Scaling up Data Augmentation MCMC via Calibration JMLR 2018 SEGA: Variance Reduction via Gradient Sketching NeurIPS 2018 SGD and Hogwild! Convergence Without the Bounded Gradients Assumption ICML 2018 Solving Non-smooth Constrained Programs with Lower Complexity than O(1/ε): A Primal-Dual Homotopy Smoothing Approach NeurIPS 2018 Sparsified SGD with Memory NeurIPS 2018 State Space Gaussian Processes with Non-Gaussian Likelihood ICML 2018 Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes NeurIPS 2018 Stein Variational Gradient Descent Without Gradient ICML 2018 Stochastic Chebyshev Gradient Descent for Spectral Optimization NeurIPS 2018 Stochastic Expectation Maximization with Variance Reduction NeurIPS 2018 Stochastic PCA with ℓ_2 and ℓ_1 Regularization ICML 2018 Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity NeurIPS 2018 Stochastic Proximal Algorithms for AUC Maximization ICML 2018 Stochastic Variance-Reduced Hamilton Monte Carlo Methods ICML 2018 The Lingering of Gradients: How to Reuse Gradients Over Time NeurIPS 2018 The Many Faces of Exponential Weights in Online Learning COLT 2018 The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning ICML 2018 Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima NeurIPS 2018 Towards Memory-Friendly Deterministic Incremental Gradient Method AISTATS 2018 Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization NeurIPS 2018 Training Deep Models Faster with Robust, Approximate Importance Sampling NeurIPS 2018 Variance-Reduced Stochastic Gradient Descent on Streaming Data NeurIPS 2018