convex optimization

From Nesterov’s Estimate Sequence to Riemannian Acceleration COLT 2020 Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization NeurIPS 2020 Gradient descent follows the regularization path for general losses COLT 2020 Gradient Perturbation is Underrated for Differentially Private Convex Optimization IJCAI 2020 Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent JMLR 2020 Hard Shape-Constrained Kernel Machines NeurIPS 2020 Highly smooth minimization of non-smooth problems COLT 2020 How Good is SGD with Random Shuffling? COLT 2020 How to Characterize The Landscape of Overparameterized Convolutional Neural Networks NeurIPS 2020 Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization ICML 2020 Importance Sampling via Local Sensitivity AISTATS 2020 Improved Algorithms for Convex-Concave Minimax Optimization NeurIPS 2020 Inexact Tensor Methods with Dynamic Accuracies ICML 2020 In Perfect Shape: Certifiably Optimal 3D Shape Reconstruction From 2D Landmarks CVPR 2020 Is Local SGD Better than Minibatch SGD? ICML 2020 Lagrangian Decomposition for Neural Network Verification UAI 2020 Large-Scale Methods for Distributionally Robust Optimization NeurIPS 2020 Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems COLT 2020 Learning by Minimizing the Sum of Ranked Range NeurIPS 2020 Learning Hierarchical Interactions at Scale: A Convex Optimization Approach AISTATS 2020 Learning with Fenchel-Young losses JMLR 2020 Learning With Subquadratic Regularization : A Primal-Dual Approach IJCAI 2020 Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming ICML 2020 Linearly Converging Error Compensated SGD NeurIPS 2020 Lower Bounds for Parallel and Randomized Convex Optimization JMLR 2020