conftrace_

strong convexity

59 papers

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Co-occurring keywords

convex optimization (1321) linear convergence (158) strongly convex (89) stochastic optimization (1060) stochastic gradient descent (1091) convergence rate (607) gradient descent (1144) regret bound (1926) stochastic gradient (296) stochastic convex optimization (69)

Papers

Universal Online Convex Optimization Meets Second-order Bounds JMLR 2025
Accelerated Methods with Compressed Communications for Distributed Optimization Problems Under Data Similarity AAAI 2025
Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization NIPS 2024
Faster Accelerated First-order Methods for Convex Optimization with Strongly Convex Function Constraints NIPS 2024
Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions JMLR 2024
Universal Online Convex Optimization with $1$ Projection per Round NIPS 2024
The Power of Extrapolation in Federated Learning NIPS 2024
Derivatives of Stochastic Gradient Descent in parametric optimization NIPS 2024
Linearly Constrained Bilevel Optimization: A Smoothed Implicit Gradient Approach ICML 2023
Faster Rates of Private Stochastic Convex Optimization ALT 2022
Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization AISTATS 2022
Projection-free Distributed Online Learning with Sublinear Communication Complexity JMLR 2022
Quasi-Newton Methods for Saddle Point Problems NIPS 2022
Convergence of Langevin Monte Carlo in Chi-Squared and Rényi Divergence AISTATS 2022
Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution NIPS 2022
Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling NIPS 2022
A Simple yet Universal Strategy for Online Convex Optimization ICML 2022
Breaking the Convergence Barrier: Optimization via Fixed-Time Convergent Flows AAAI 2022
Dueling Convex Optimization ICML 2021
Parameter-free Locally Accelerated Conditional Gradients ICML 2021
A Study of Condition Numbers for First-Order Optimization AISTATS 2021
From Low Probability to High Confidence in Stochastic Convex Optimization JMLR 2021
Distributed Zero-Order Optimization under Adversarial Noise NIPS 2021
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients NIPS 2021
Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks NIPS 2021