Cover Image

On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liang, Ling, Yang, Haizhao
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

PINS: Proximal Iterations with Sparse Newton and Sinkhorn for Optimal Transport
by: Wu, Di, et al.
Published: (2025)

Variance Reduction and Low Sample Complexity in Stochastic Optimization via Proximal Point Method
by: Liang, Jiaming
Published: (2024)

Accelerating Multi-Block Constrained Optimization Through Learning to Optimize
by: Liang, Ling, et al.
Published: (2024)

A Variance-Reduced Stochastic Gradient Tracking Algorithm for Decentralized Optimization with Orthogonality Constraints
by: Wang, Lei, et al.
Published: (2022)

Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods
by: Chayti, El Mahdi, et al.
Published: (2023)

Breaking the Stochasticity Barrier: An Adaptive Variance-Reduced Method for Variational Inequalities
by: Jeong, Yungi, et al.
Published: (2026)

Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning
by: Ding, Kuangyu, et al.
Published: (2023)

Locally Regularized Sparse Graph by Fast Proximal Gradient Descent
by: Sun, Dongfang, et al.
Published: (2024)

Curse of Dimensionality in Neural Network Optimization
by: Na, Sanghoon, et al.
Published: (2025)

Stochastic Gradient Langevin Dynamics with Variance Reduction
by: Huang, Zhishen, et al.
Published: (2021)

Projection-Free Variance Reduction Methods for Stochastic Constrained Multi-Level Compositional Optimization
by: Jiang, Wei, et al.
Published: (2024)

Beyond Bounded Variance: Variance-Reduced Normalized Methods for Nonconvex Optimization under Blum-Gladyshev Noise
by: Upadhyay, Antesh, et al.
Published: (2026)

A Proximal Modified Quasi-Newton Method for Nonsmooth Regularized Optimization
by: Diouane, Youssef, et al.
Published: (2024)

VFOG: Variance-Reduced Fast Optimistic Gradient Methods for a Class of Nonmonotone Generalized Equations
by: Tran-Dinh, Quoc, et al.
Published: (2025)

Gradient Estimation and Variance Reduction in Stochastic and Deterministic Models
by: Keane, Ronan
Published: (2024)

Stochastic Newton Proximal Extragradient Method
by: Jiang, Ruichen, et al.
Published: (2024)

Adaptive Proximal Gradient Method for Convex Optimization
by: Malitsky, Yura, et al.
Published: (2023)

Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions
by: Jiang, Wei, et al.
Published: (2024)

Riemannian Stochastic Gradient Method for Nested Composition Optimization
by: Zhang, Dewei, et al.
Published: (2022)

Variance-Reduced $(\varepsilon,δ)-$Unlearning using Forget Set Gradients
by: Van Waerebeke, Martin, et al.
Published: (2026)

Towards Weaker Variance Assumptions for Stochastic Optimization
by: Alacaoglu, Ahmet, et al.
Published: (2025)

A Proximal Gradient Method With Probabilistic Multi-Gossip Communications for Decentralized Composite Optimization
by: Guo, Luyao, et al.
Published: (2023)

Momentum Does Not Reduce Stochastic Noise in Stochastic Gradient Descent
by: Sato, Naoki, et al.
Published: (2024)

Adaptive Optimization via Momentum on Variance-Normalized Gradients
by: Patitucci, Francisco, et al.
Published: (2026)

Faster Gradient Methods for Highly-Smooth Stochastic Bilevel Optimization
by: Chen, Lesi, et al.
Published: (2025)

Compressed Decentralized Momentum Stochastic Gradient Methods for Nonconvex Optimization
by: Liu, Wei, et al.
Published: (2025)

Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches
by: Dereziński, Michał
Published: (2022)

Decentralized Non-convex Stochastic Optimization with Heterogeneous Variance
by: Chen, Hongxu, et al.
Published: (2026)

TRSVR: An Adaptive Stochastic Trust-Region Method with Variance Reduction
by: Fang, Yuchen, et al.
Published: (2026)

Proximal Oracles for Optimization and Sampling
by: Liang, Jiaming, et al.
Published: (2024)

Stochastic Gradient Methods with Preconditioned Updates
by: Sadiev, Abdurakhmon, et al.
Published: (2022)

Multi-level Monte-Carlo Gradient Methods for Stochastic Optimization with Biased Oracles
by: Hu, Yifan, et al.
Published: (2024)

On the Global Convergence of Risk-Averse Natural Policy Gradient Methods with Expected Conditional Risk Measures
by: Yu, Xian, et al.
Published: (2023)

Stabilized Proximal-Point Methods for Federated Optimization
by: Jiang, Xiaowen, et al.
Published: (2024)

A Unified Theory of Stochastic Proximal Point Methods without Smoothness
by: Richtárik, Peter, et al.
Published: (2024)

Dynamic Proximal Gradient Algorithms for Schatten-$p$ Quasi-Norm Regularized Problems
by: Shen, Weiping, et al.
Published: (2026)

SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning
by: Karagulyan, Avetik, et al.
Published: (2024)

Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints
by: Ablin, Pierre, et al.
Published: (2023)

Nested Stochastic Algorithm for Generalized Sinkhorn distance-Regularized Distributionally Robust Optimization
by: Yang, Yufeng, et al.
Published: (2025)

Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning
by: Bylinkin, Dmitry, et al.
Published: (2024)