Cover Image

A New Stochastic Approximation Method for Gradient-based Simulated Parameter Estimation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Zehao, Peng, Yijie
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Stochastic Approximation Methods for Distortion Risk Measure Optimization
by: Jiang, Jinyang, et al.
Published: (2025)

Beyond likelihood ratio bias: Nested multi-time-scale stochastic approximation for likelihood-free parameter estimation
by: Li, Zehao, et al.
Published: (2024)

Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment
by: Zheng, Yi, et al.
Published: (2024)

Optimal low-rank stochastic gradient estimation for LLM training
by: Li, Zehao, et al.
Published: (2026)

First and Second Order Approximations to Stochastic Gradient Descent Methods with Momentum Terms
by: Lu, Eric
Published: (2025)

Stochastic Gradient Variational Inference with Price's Gradient Estimator from Bures-Wasserstein to Parameter Space
by: Kim, Kyurae, et al.
Published: (2026)

Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent
by: Ziyin, Liu, et al.
Published: (2024)

Gradient Estimation Methods of Approximate Multipliers for High-Accuracy Retraining of Deep Learning Models
by: Meng, Chang, et al.
Published: (2025)

Robust Approximate Sampling via Stochastic Gradient Barker Dynamics
by: Mauri, Lorenzo, et al.
Published: (2024)

Posterior Approximation using Stochastic Gradient Ascent with Adaptive Stepsize
by: Lim, Kart-Leong, et al.
Published: (2024)

Nonparametric Instrumental Variable Regression through Stochastic Approximate Gradients
by: Fonseca, Yuri, et al.
Published: (2024)

Multiple Importance Sampling for Stochastic Gradient Estimation
by: Salaün, Corentin, et al.
Published: (2024)

Generalizing Stochastic Smoothing for Differentiation and Gradient Estimation
by: Petersen, Felix, et al.
Published: (2024)

Inverting the Leverage Score Gradient: An Efficient Approximate Newton Method
by: Li, Chenyang, et al.
Published: (2024)

Central Limit Theorems for Stochastic Gradient Descent Quantile Estimators
by: Wei, Ziyang, et al.
Published: (2025)

Entropy-regularized Gradient Estimators for Approximate Bayesian Inference
by: Kaur, Jasmeet
Published: (2025)

Asymptotic Time-Uniform Inference for Parameters in Averaged Stochastic Approximation
by: Xie, Chuhan, et al.
Published: (2024)

Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
by: Li, Tianyou, et al.
Published: (2023)

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

Efficient CNN-LSTM based Parameter Estimation of Levy Driven Stochastic Differential Equations
by: Li, Shuaiyu, et al.
Published: (2024)

The Dimension Strikes Back with Gradients: Generalization of Gradient Methods in Stochastic Convex Optimization
by: Schliserman, Matan, et al.
Published: (2024)

Towards Continuous-Time Approximations for Stochastic Gradient Descent without Replacement
by: Perko, Stefan
Published: (2025)

Asynch-SGBDT: Asynchronous Parallel Stochastic Gradient Boosting Decision Tree based on Parameters Server
by: Daning, Cheng, et al.
Published: (2018)

Online Covariance Estimation in Nonsmooth Stochastic Approximation
by: Jiang, Liwei, et al.
Published: (2025)

Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo
by: Kim, Hyunsu, et al.
Published: (2025)

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

Secure Generalization through Stochastic Bidirectional Parameter Updates Using Dual-Gradient Mechanism
by: Goel, Shourya, et al.
Published: (2025)

Revisiting Stochastic Approximation and Stochastic Gradient Descent
by: Karandikar, Rajeeva Laxman, et al.
Published: (2025)

Beyond Exact Gradients: Convergence of Stochastic Soft-Max Policy Gradient Methods with Entropy Regularization
by: Ding, Yuhao, et al.
Published: (2021)

Stochastic Attention: Connectome-Inspired Randomized Routing for Expressive Linear-Time Attention
by: Jin, Zehao, et al.
Published: (2026)

Convergence of the Stochastic Heavy Ball Method With Approximate Gradients and/or Block Updating
by: Tadipatri, Uday Kiran Reddy, et al.
Published: (2023)

Matrix Low-Rank Approximation For Policy Gradient Methods
by: Rozada, Sergio, et al.
Published: (2024)

Stochastic Normalized Gradient Descent with Momentum for Large-Batch Training
by: Zhao, Shen-Yi, et al.
Published: (2020)

Policy Gradient Methods for Discrete Time Linear Quadratic Regulator With Random Parameters
by: Li, Deyue
Published: (2023)

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

On Cold Posteriors of Probabilistic Neural Networks: Understanding the Cold Posterior Effect and A New Way to Learn Cold Posteriors with Tight Generalization Guarantees
by: Zhang, Yijie
Published: (2024)

On the Role of Batch Size in Stochastic Conditional Gradient Methods
by: Islamov, Rustem, et al.
Published: (2026)

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

Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods
by: Liu, Zijian, et al.
Published: (2023)

Markov Chain Variance Estimation: A Stochastic Approximation Approach
by: Agrawal, Shubhada, et al.
Published: (2024)