stochastic gradient descent

Perishability of Data: Dynamic Pricing under Varying-Coefficient Models JMLR 2017 Probabilistic Line Searches for Stochastic Optimization JMLR 2017 QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding NeurIPS 2017 Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization NeurIPS 2017 Relativistic Monte Carlo AISTATS 2017 Safe Adaptive Importance Sampling NeurIPS 2017 Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling ACL 2017 SGD Learns the Conjugate Kernel Class of the Network NeurIPS 2017 Sparse Communication for Distributed Gradient Descent EMNLP 2017 Stabilized Sparse Online Learning for Sparse Data JMLR 2017 Stochastic Generative Hashing ICML 2017 Stochastic Gradient Descent as Approximate Bayesian Inference JMLR 2017 Stochastic Gradient MCMC Methods for Hidden Markov Models ICML 2017 Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms ICML 2017 Stochastic Online Anomaly Analysis for Streaming Time Series IJCAI 2017 The Marginal Value of Adaptive Gradient Methods in Machine Learning NeurIPS 2017 The Scaling Limit of High-Dimensional Online Independent Component Analysis NeurIPS 2017 Training Deep Networks without Learning Rates Through Coin Betting NeurIPS 2017 Train longer, generalize better: closing the generalization gap in large batch training of neural networks NeurIPS 2017 Tux²: Distributed Graph Computation for Machine Learning NSDI 2017 Accelerated Stochastic Gradient Descent for Minimizing Finite Sums AISTATS 2016 Adaptive Sampling for SGD by Exploiting Side Information ICML 2016 A Light Touch for Heavily Constrained SGD COLT 2016 A Multi-Batch L-BFGS Method for Machine Learning NeurIPS 2016 A Variational Analysis of Stochastic Gradient Algorithms ICML 2016