stochastic gradient descent

Conditional Accelerated Lazy Stochastic Gradient Descent ICML 2017 Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models AISTATS 2017 Convergence Analysis of Two-layer Neural Networks with ReLU Activation NeurIPS 2017 Deep Hyperalignment NeurIPS 2017 Differentially Private Empirical Risk Minimization Revisited: Faster and More General NeurIPS 2017 Efficient Private ERM for Smooth Objectives IJCAI 2017 Empirical Evaluation of Parallel Training Algorithms on Acoustic Modeling Interspeech 2017 Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$- and $O(1/n^2)$-type of Risk Bounds COLT 2017 Fast Parallel Training of Neural Language Models IJCAI 2017 Fast Residual Forests: Rapid Ensemble Learning for Semantic Segmentation CoRL 2017 Forward and Reverse Gradient-Based Hyperparameter Optimization ICML 2017 Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression JMLR 2017 Large-scale Online Kernel Learning with Random Feature Reparameterization IJCAI 2017 Less than a Single Pass: Stochastically Controlled Stochastic Gradient AISTATS 2017 Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent NeurIPS 2017 Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox COLT 2017 Mini-batch Block-coordinate based Stochastic Average Adjusted Gradient Methods to Solve Big Data Problems ACML 2017 Model Accuracy and Runtime Tradeoff in Distributed Deep Learning: A Systematic Study IJCAI 2017 Multiscale Quantization for Fast Similarity Search NeurIPS 2017 Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation CVPR 2017 Non-convex Finite-Sum Optimization Via SCSG Methods NeurIPS 2017 Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability ICML 2017 Online to Offline Conversions, Universality and Adaptive Minibatch Sizes NeurIPS 2017 On Structured Prediction Theory with Calibrated Convex Surrogate Losses NeurIPS 2017 Optimal Rates for Multi-pass Stochastic Gradient Methods JMLR 2017