strongly convex optimization

An Optimal Algorithm for Strongly Convex Min-Min Optimization UAI 2025 Optimal Complexity in Byzantine-Robust Distributed Stochastic Optimization with Data Heterogeneity JMLR 2025 Gradient-free optimization of highly smooth functions: improved analysis and a new algorithm JMLR 2024 Tight Rates for Bandit Control Beyond Quadratics NeurIPS 2024 Unifying Nesterov’s Accelerated Gradient Methods for Convex and Strongly Convex Objective Functions ICML 2023 Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution NeurIPS 2022 ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm COLT 2022 Stochastic Reweighted Gradient Descent ICML 2022 Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent ICML 2022 Variance Reduced EXTRA and DIGing and Their Optimal Acceleration for Strongly Convex Decentralized Optimization JMLR 2022 Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance NeurIPS 2021 Gradient Descent Averaging and Primal-dual Averaging for Strongly Convex Optimization AAAI 2021 Implicit Langevin Algorithms for Sampling From Log-concave Densities JMLR 2021 Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks NeurIPS 2021 Complexity Guarantees for Polyak Steps with Momentum COLT 2020 On the Convergence of Nesterov’s Accelerated Gradient Method in Stochastic Settings ICML 2020 On the Convergence of SARAH and Beyond AISTATS 2020 New Convergence Aspects of Stochastic Gradient Algorithms JMLR 2019 Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD NeurIPS 2019 Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization AISTATS 2018 SADAGRAD: Strongly Adaptive Stochastic Gradient Methods ICML 2018 Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks ICML 2017 On Lower and Upper Bounds in Smooth and Strongly Convex Optimization JMLR 2016 The Benefits of Learning with Strongly Convex Approximate Inference ICML 2015 Bandit Convex Optimization: Towards Tight Bounds NeurIPS 2014