conftrace_

strongly convex optimization

23 papers

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Co-occurring keywords

stochastic gradient descent (1091) convergence rate (607) convex optimization (1321) strongly convex (89) variance reduction (523) gradient descent (1144) linear convergence (158) lower bound (173) nesterov acceleration (54) network optimization (80)

Papers

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
Unifying Nesterov’s Accelerated Gradient Methods for Convex and Strongly Convex Objective Functions ICML 2023
ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm COLT 2022
Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent ICML 2022
Stochastic Reweighted 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 NIPS 2021
Gradient Descent Averaging and Primal-dual Averaging for Strongly Convex Optimization AAAI 2021
Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks NIPS 2021
Complexity Guarantees for Polyak Steps with Momentum COLT 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 NIPS 2019
SADAGRAD: Strongly Adaptive Stochastic Gradient Methods ICML 2018
Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization AISTATS 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
Beyond the Regret Minimization Barrier: Optimal Algorithms for Stochastic Strongly-Convex Optimization JMLR 2014
Linear Convergence with Condition Number Independent Access of Full Gradients NIPS 2013
Accelerating Stochastic Gradient Descent using Predictive Variance Reduction NIPS 2013
A Stochastic Gradient Method with an Exponential Convergence _Rate for Finite Training Sets NIPS 2012