learning rate

Understanding Batch Normalization NeurIPS 2018 Understanding Short-Horizon Bias in Stochastic Meta-Optimization ICLR 2018 When is a Convolutional Filter Easy to Learn? ICLR 2018 Boltzmann Exploration Done Right NeurIPS 2017 Distributed Learning with Regularized Least Squares JMLR 2017 Generalization Properties of Learning with Random Features NeurIPS 2017 Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network JMLR 2017 Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos NeurIPS 2017 Optimal Rates for Multi-pass Stochastic Gradient Methods JMLR 2017 Training Deep Networks without Learning Rates Through Coin Betting NeurIPS 2017 Fast learning rates with heavy-tailed losses NeurIPS 2016 Learning under uncertainty: a comparison between R-W and Bayesian approach NeurIPS 2016 MetaGrad: Multiple Learning Rates in Online Learning NeurIPS 2016 On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability NeurIPS 2016 On the Influence of Momentum Acceleration on Online Learning JMLR 2016 Optimal Learning for Multi-pass Stochastic Gradient Methods NeurIPS 2016 Optimal Learning Rates for Localized SVMs JMLR 2016 Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics JMLR 2016 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift ICML 2015 Learning from Data with Heterogeneous Noise using SGD AISTATS 2015 Second-order Quantile Methods for Experts and Combinatorial Games COLT 2015 Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning NeurIPS 2014 From Stochastic Mixability to Fast Rates NeurIPS 2014 Learning the Learning Rate for Prediction with Expert Advice NeurIPS 2014 Adaptive Step-Size for Policy Gradient Methods NeurIPS 2013