Cover Image

Stabilizing Policy Gradients for Stochastic Differential Equations via Consistency with Perturbation Process

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhou, Xiangxin, Wang, Liang, Zhou, Yichi
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Riemannian Consistency Model
by: Cheng, Chaoran, et al.
Published: (2025)

Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent
by: Schertzer, Adrien, et al.
Published: (2024)

Integrating Protein Dynamics into Structure-Based Drug Design via Full-Atom Stochastic Flows
by: Zhou, Xiangxin, et al.
Published: (2025)

Stabilization of Perturbed Loss Function: Differential Privacy without Gradient Noise
by: Habib, Salman, et al.
Published: (2025)

Euphonium: Steering Video Flow Matching via Process Reward Gradient Guided Stochastic Dynamics
by: Zhong, Ruizhe, et al.
Published: (2026)

Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations
by: Xu, Jian, et al.
Published: (2024)

Weighted Stochastic Differential Equation to Implement Wasserstein-Fisher-Rao Gradient Flow
by: Rahimi, Herlock
Published: (2025)

Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations
by: Xue, Kaiwen, et al.
Published: (2024)

Differentially Private Policy Gradient
by: Rio, Alexandre, et al.
Published: (2025)

Bridging Text and Molecule: A Survey on Multimodal Frameworks for Molecule
by: Xiao, Yi, et al.
Published: (2024)

Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization
by: Zhou, Xiangxin, et al.
Published: (2024)

Distributed Stochastic Gradient Descent with Staleness: A Stochastic Delay Differential Equation Based Framework
by: Yu, Siyuan, et al.
Published: (2024)

Learning Optimal Deterministic Policies with Stochastic Policy Gradients
by: Montenegro, Alessandro, et al.
Published: (2024)

DP-CSGP: Differentially Private Stochastic Gradient Push with Compressed Communication
by: Zhu, Zehan, et al.
Published: (2025)

Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations
by: Bergna, Richard, et al.
Published: (2024)

Scale-Consistent Learning for Partial Differential Equations
by: Li, Zongyi, et al.
Published: (2025)

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

A Hessian-Aware Stochastic Differential Equation for Modelling SGD
by: Li, Xiang, et al.
Published: (2024)

Neural Structure Learning with Stochastic Differential Equations
by: Wang, Benjie, et al.
Published: (2023)

Sample from What You See: Visuomotor Policy Learning via Diffusion Bridge with Observation-Embedded Stochastic Differential Equation
by: Liu, Zhaoyang, et al.
Published: (2025)

Towards Identifiability of Interventional Stochastic Differential Equations
by: Zweig, Aaron, et al.
Published: (2025)

Gradient Perturbation: Learning to Perturb Gradients for Adaptive Training
by: Li, Hua
Published: (2026)

Stabilizing Policy Gradient Methods via Reward Profiling
by: Ahmed, Shihab, et al.
Published: (2025)

On the Convergence of Stochastic Gradient Descent with Perturbed Forward-Backward Passes
by: Kong, Boao, et al.
Published: (2026)

Expanding the Chaos: Neural Operator for Stochastic (Partial) Differential Equations
by: Shi, Dai, et al.
Published: (2026)

Variational Neural Stochastic Differential Equations with Change Points
by: El-Laham, Yousef, et al.
Published: (2024)

Spatio-Temporal Graph Deep Learning with Stochastic Differential Equations for Uncovering Alzheimer's Disease Progression
by: Zhou, Houliang, et al.
Published: (2025)

Perturbation Analysis of Randomized SVD and its Applications to Statistics
by: Zhang, Yichi, et al.
Published: (2022)

Generative Modeling of Neural Dynamics via Latent Stochastic Differential Equations
by: ElGazzar, Ahmed, et al.
Published: (2024)

Learning to Generate Formally Verifiable Step-by-Step Logic Reasoning via Structured Formal Intermediaries
by: Chen, Luoxin, et al.
Published: (2026)

Component Fourier Neural Operator for Singularly Perturbed Differential Equations
by: Li, Ye, et al.
Published: (2024)

Parameter Estimation in Stochastic Differential Equations via Wiener Chaos Expansion and Stochastic Gradient Descent
by: Delgado-Vences, Francisco, et al.
Published: (2026)

Statistical Inference for Differentially Private Stochastic Gradient Descent
by: Xia, Xintao, et al.
Published: (2025)

Reusing Historical Trajectories in Natural Policy Gradient via Importance Sampling: Convergence and Convergence Rate
by: Lin, Yifan, et al.
Published: (2024)

Sampling in High-Dimensions using Stochastic Interpolants and Forward-Backward Stochastic Differential Equations
by: George, Anand Jerry, et al.
Published: (2025)

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

Improving the Noise Estimation of Latent Neural Stochastic Differential Equations
by: Heck, Linus, et al.
Published: (2024)

Drift Estimation for Stochastic Differential Equations with Denoising Diffusion Models
by: Costa, Marcos Tapia, et al.
Published: (2026)

In-Context Learning of Stochastic Differential Equations with Foundation Inference Models
by: Seifner, Patrick, et al.
Published: (2025)

Reprogramming Pretrained Target-Specific Diffusion Models for Dual-Target Drug Design
by: Zhou, Xiangxin, et al.
Published: (2024)