strongly convex function

Black-Box Reductions for Decentralized Online Convex Optimization in Changing Environments COLT 2025 Optimal Decentralized Composite Optimization for Strongly Convex Functions JMLR 2025 Almost Sure Convergence Rates Analysis and Saddle Avoidance of Stochastic Gradient Methods JMLR 2024 A Simple and Optimal Approach for Universal Online Learning with Gradient Variations NeurIPS 2024 Estimating the Minimizer and the Minimum Value of a Regression Function under Passive Design JMLR 2024 Faster Accelerated First-order Methods for Convex Optimization with Strongly Convex Function Constraints NeurIPS 2024 Gradient-free optimization of highly smooth functions: improved analysis and a new algorithm JMLR 2024 Universal Online Convex Optimization with $1$ Projection per Round NeurIPS 2024 User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates AISTATS 2024 Large deviations rates for stochastic gradient descent with strongly convex functions AISTATS 2023 On a Class of Gibbs Sampling over Networks COLT 2023 Adaptive Bandit Convex Optimization with Heterogeneous Curvature COLT 2022 On Almost Sure Convergence Rates of Stochastic Gradient Methods COLT 2022 Permutation-Based SGD: Is Random Optimal? ICLR 2022 Improved Analysis for Dynamic Regret of Strongly Convex and Smooth Functions L4DC 2021 On Online Optimization: Dynamic Regret Analysis of Strongly Convex and Smooth Problems AAAI 2021 Revisiting Projection-free Online Learning: the Strongly Convex Case AISTATS 2021 Skew-symmetrically perturbed gradient flow for convex optimization ACML 2021 Closing the convergence gap of SGD without replacement ICML 2020 Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits NeurIPS 2020 Linear Convergence of Adaptive Stochastic Gradient Descent AISTATS 2020 Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization NeurIPS 2020 Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions JMLR 2020 SAdam: A Variant of Adam for Strongly Convex Functions ICLR 2020 Trading-Off Static and Dynamic Regret in Online Least-Squares and Beyond AAAI 2020