conftrace_

linear convergence

158 papers

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Co-occurring keywords

convex optimization (1321) gradient descent (1144) stochastic optimization (1060) variance reduction (523) stochastic gradient (296) stochastic gradient descent (1091) strong convexity (59) frank-wolfe algorithm (107) coordinate descent (147) support vector machine (978)

Papers

On the Convergence of Tâtonnement for Linear Fisher Markets AAAI 2025
Linear Trading Position with Sparse Spectrum IJCAI 2025
Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients JMLR 2025
Inexact Augmented Lagrangian Methods for Conic Optimization: Quadratic Growth and Linear Convergence NIPS 2024
PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates JMLR 2024
On the Hyperparameters in Stochastic Gradient Descent with Momentum JMLR 2024
Large-Scale Gaussian Processes via Alternating Projection AISTATS 2024
Convergence of $\text{log}(1/\epsilon)$ for Gradient-Based Algorithms in Zero-Sum Games without the Condition Number: A Smoothed Analysis NIPS 2024
Policy Mirror Descent with Lookahead NIPS 2024
DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representations AAAI 2024
Error bounds, PL condition, and quadratic growth for weakly convex functions, and linear convergences of proximal point methods L4DC 2024
Achieving Linear Convergence with Parameter-Free Algorithms in Decentralized Optimization NIPS 2024
Policy Optimization for Robust Average Reward MDPs NIPS 2024
Global Convergence of Over-parameterized Deep Equilibrium Models AISTATS 2023
A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence NIPS 2023
Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer Evaluations ICML 2023
Gradient Descent Converges Linearly for Logistic Regression on Separable Data ICML 2023
On a Class of Gibbs Sampling over Networks COLT 2023
Stochastic Optimization for Spectral Risk Measures AISTATS 2023
Algorithm for Constrained Markov Decision Process with Linear Convergence AISTATS 2023
FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks ICML 2023
The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing ICML 2023
Incremental Aggregated Riemannian Gradient Method for Distributed PCA AISTATS 2023
On Biased Compression for Distributed Learning JMLR 2023
Stochastic Mirror Descent for Large-Scale Sparse Recovery AISTATS 2023