strong convexity

Dueling Convex Optimization ICML 2021 Fast rates for prediction with limited expert advice NeurIPS 2021 From Low Probability to High Confidence in Stochastic Convex Optimization JMLR 2021 Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients NeurIPS 2021 Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks NeurIPS 2021 Parameter-free Locally Accelerated Conditional Gradients ICML 2021 Revisiting Projection-free Online Learning: the Strongly Convex Case AISTATS 2021 The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks COLT 2021 Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates NeurIPS 2020 Complexity Guarantees for Polyak Steps with Momentum COLT 2020 Learning Interpretable Models using Soft Integrity Constraints ACML 2020 Linearly Convergent Frank-Wolfe with Backtracking Line-Search AISTATS 2020 Near-Optimal Algorithms for Minimax Optimization COLT 2020 Random Reshuffling: Simple Analysis with Vast Improvements NeurIPS 2020 Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport AISTATS 2020 Stochastic Optimization for Performative Prediction NeurIPS 2020 Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems without Strong Convexity AISTATS 2019 Mixing of Hamiltonian Monte Carlo on strongly log-concave distributions 2: Numerical integrators AISTATS 2019 Stochastic Approximation of Smooth and Strongly Convex Functions: Beyond the $O(1/T)$ Convergence Rate COLT 2019 Taming the Noisy Gradient: Train Deep Neural Networks with Small Batch Sizes IJCAI 2019 Batch-Expansion Training: An Efficient Optimization Framework AISTATS 2018 Convergence of Langevin MCMC in KL-divergence ALT 2018 SADAGRAD: Strongly Adaptive Stochastic Gradient Methods ICML 2018 Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter NeurIPS 2017 Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$- and $O(1/n^2)$-type of Risk Bounds COLT 2017