convergence rate

On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics JMLR 2020 On the Convergence of FedAvg on Non-IID Data ICLR 2020 On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning AAAI 2020 On the Global Convergence Rates of Softmax Policy Gradient Methods ICML 2020 Open Problem: Tight Convergence of SGD in Constant Dimension COLT 2020 Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization NeurIPS 2020 Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform NeurIPS 2020 Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes COLT 2020 Optimal Randomized First-Order Methods for Least-Squares Problems ICML 2020 Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities NeurIPS 2020 pbSGD: Powered Stochastic Gradient Descent Methods for Accelerated Non-Convex Optimization IJCAI 2020 Personalized Federated Learning with Moreau Envelopes NeurIPS 2020 Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization IJCAI 2020 Random Reshuffling: Simple Analysis with Vast Improvements NeurIPS 2020 Recht-Re Noncommutative Arithmetic-Geometric Mean Conjecture is False ICML 2020 Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations NeurIPS 2020 SGD with shuffling: optimal rates without component convexity and large epoch requirements NeurIPS 2020 Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models AISTATS 2020 SPAN: A Stochastic Projected Approximate Newton Method AAAI 2020 Statistical Guarantees of Distributed Nearest Neighbor Classification NeurIPS 2020 Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization ICML 2020 Stochastic Gradient and Langevin Processes ICML 2020 The Error-Feedback framework: SGD with Delayed Gradients JMLR 2020 Theory of Curriculum Learning, with Convex Loss Functions JMLR 2020 Tight Convergence Rate of Gradient Descent for Eigenvalue Computation IJCAI 2020