Optimization

Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions NeurIPS 2019 Accelerating Sparse Matrix Operations in Neural Networks on Graphics Processing Units ACL 2019 Acceleration of SVRG and Katyusha X by Inexact Preconditioning ICML 2019 Acceleration via Symplectic Discretization of High-Resolution Differential Equations NeurIPS 2019 Accurate, reliable and fast robustness evaluation NeurIPS 2019 A Closed-Form Solution to Universal Style Transfer ICCV 2019 A Communication Efficient Stochastic Multi-Block Alternating Direction Method of Multipliers NeurIPS 2019 A Composite Randomized Incremental Gradient Method ICML 2019 A Condition Number for Joint Optimization of Cycle-Consistent Networks NeurIPS 2019 A Continuous-Time View of Early Stopping for Least Squares Regression AISTATS 2019 A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks ICLR 2019 A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks NeurIPS 2019 A Convex Relaxation for Multi-Graph Matching CVPR 2019 Acoustic Non-Line-Of-Sight Imaging CVPR 2019 Active Mini-Batch Sampling Using Repulsive Point Processes AAAI 2019 Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization AISTATS 2019 AdaGrad Stepsizes: Sharp Convergence Over Nonconvex Landscapes ICML 2019 Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces ICML 2019 Adaptive Antithetic Sampling for Variance Reduction ICML 2019 Adaptive Gradient-Based Meta-Learning Methods NeurIPS 2019 Adaptive Gradient Methods with Dynamic Bound of Learning Rate ICLR 2019 Adaptive Hashing for Model Counting UAI 2019 Adaptive Optimization Framework for Control of Multi-Agent Systems AAAI 2019 Adaptive Proximal Average Based Variance Reducing Stochastic Methods for Optimization with Composite Regularization AAAI 2019 Adaptive Regret of Convex and Smooth Functions ICML 2019