Cover Image

Functional Gradient Flows for Constrained Sampling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Shiyue, Yu, Longlin, Cheng, Ziheng, Zhang, Cheng
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

Semi-Implicit Functional Gradient Flow for Efficient Sampling
by: Zhang, Shiyue, et al.
Published: (2024)

A Kernel Approach for Semi-implicit Variational Inference
by: Yu, Longlin, et al.
Published: (2026)

Kernel Semi-Implicit Variational Inference
by: Cheng, Ziheng, et al.
Published: (2024)

Reflected Flow Matching
by: Xie, Tianyu, et al.
Published: (2024)

Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learning
by: Cheng, Ziheng, et al.
Published: (2025)

Diffusion-PINN Sampler
by: Shi, Zhekun, et al.
Published: (2024)

Deterministic Policy Gradient for Reinforcement Learning with Continuous Time and State
by: Cheng, Ziheng, et al.
Published: (2025)

Continuous Semi-Implicit Models
by: Yu, Longlin, et al.
Published: (2025)

Data-Efficient Training by Evolved Sampling
by: Cheng, Ziheng, et al.
Published: (2025)

Stochastically Constrained Best Arm Identification with Thompson Sampling
by: Yang, Le, et al.
Published: (2025)

GANs as Gradient Flows that Converge
by: Huang, Yu-Jui, et al.
Published: (2022)

Gradient-Free Generation for Hard-Constrained Systems
by: Cheng, Chaoran, et al.
Published: (2024)

CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling
by: Zhang, Kaiyuan, et al.
Published: (2025)

Convergence of Distributed Adaptive Optimization with Local Updates
by: Cheng, Ziheng, et al.
Published: (2024)

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators
by: Wang, Longlin, et al.
Published: (2025)

Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context
by: Cheng, Xiang, et al.
Published: (2023)

Optimal Confidence Band for Kernel Gradient Flow Estimator
by: Cheng, Yuqian, et al.
Published: (2026)

A Unified Framework for Data-Free One-Step Sampling via Wasserstein Gradient Flows
by: Wang, Chenguang, et al.
Published: (2026)

Neural Sinkhorn Gradient Flow
by: Zhu, Huminhao, et al.
Published: (2024)

Hierarchical Gradient-Based Genetic Sampling for Accurate Prediction of Biological Oscillations
by: Rao, Heng, et al.
Published: (2024)

Information-Theoretic Requirements for Gradient-Based Task Affinity Estimation in Multi-Task Learning
by: Zhang, Jasper, et al.
Published: (2026)

Stiefel Flow Matching for Moment-Constrained Structure Elucidation
by: Cheng, Austin, et al.
Published: (2024)

Robust Stochastic Gradient Posterior Sampling with Lattice Based Discretisation
by: Mensch, Zier, et al.
Published: (2026)

HardFlow: Hard-Constrained Sampling for Flow-Matching Models via Trajectory Optimization
by: Li, Zeyang, et al.
Published: (2025)

Constraint-Aware Flow Matching: Decision Aligned End-to-End Training for Constrained Sampling
by: Christopher, Jacob K., et al.
Published: (2026)

Gradient-based Discrete Sampling with Automatic Cyclical Scheduling
by: Pynadath, Patrick, et al.
Published: (2024)

Projection-Free Functional Constrained Optimization for Risk Aversion and Sparsity Control
by: Cheng, Yi, et al.
Published: (2022)

SAC Flow: Sample-Efficient Reinforcement Learning of Flow-Based Policies via Velocity-Reparameterized Sequential Modeling
by: Zhang, Yixian, et al.
Published: (2025)

Flow Straight and Fast in Hilbert Space: Functional Rectified Flow
by: Zhang, Jianxin, et al.
Published: (2025)

Faster Sampling via Stochastic Gradient Proximal Sampler
by: Huang, Xunpeng, et al.
Published: (2024)

A Gradient Guided Diffusion Framework for Chance Constrained Programming
by: Zhang, Boyang, et al.
Published: (2025)

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency
by: Zhang, Yibo Jacky, et al.
Published: (2026)

Sampling via Gradient Flows in the Space of Probability Measures
by: Chen, Yifan, et al.
Published: (2023)

Kernel Learning for Sample Constrained Black-Box Optimization
by: Rajagopalan, Rajalaxmi, et al.
Published: (2025)

Value Gradient Sampler: Learning Invariant Value Functions for Equivariant Diffusion Sampling
by: Hwang, Himchan, et al.
Published: (2025)

Why GRPO Needs Normalization: A Local-Curvature Perspective on Adaptive Gradients
by: Ge, Cheng, et al.
Published: (2026)

Conditional Lagrangian Wasserstein Flow for Time Series Imputation
by: Qian, Weizhu, et al.
Published: (2024)

Reheated Gradient-based Discrete Sampling for Combinatorial Optimization
by: Li, Muheng, et al.
Published: (2025)

Constrained Linear Thompson Sampling
by: Gangrade, Aditya, et al.
Published: (2025)

Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs
by: Ding, Dongsheng, et al.
Published: (2023)