{"total":58,"rows":[{"id":"2503.02819","title":"Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts","created":"2025-03-04","cited_by_count":96,"area":"diffusion"},{"id":"2504.11713","title":"Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching","created":"2025-04-16","cited_by_count":85,"area":"diffusion"},{"id":"2405.20971","title":"Amortizing intractable inference in diffusion models for vision, language, and control","created":"2024-05-31","cited_by_count":72,"area":"diffusion"},{"id":"2502.00361","title":"Efficient Online Reinforcement Learning for Diffusion Policy","created":"2025-02-01","cited_by_count":71,"area":"diffusion"},{"id":"2406.01661","title":"A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization","created":"2024-06-03","cited_by_count":59,"area":"diffusion"},{"id":"2502.02316","title":"DIME:Diffusion-Based Maximum Entropy Reinforcement Learning","created":"2025-02-04","cited_by_count":58,"area":"diffusion"},{"id":"2402.08667","title":"Target Score Matching","created":"2024-02-13","cited_by_count":50,"area":"diffusion"},{"id":"2502.11612","title":"Maximum Entropy Reinforcement Learning with Diffusion Policy","created":"2025-02-17","cited_by_count":34,"area":"diffusion"},{"id":"2407.01573","title":"Model-Based Diffusion for Trajectory Optimization","created":"2024-05-28","cited_by_count":32,"area":"diffusion"},{"id":"2410.08134","title":"Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction","created":"2024-10-10","cited_by_count":32,"area":"diffusion"},{"id":"2502.08696","title":"Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics","created":"2025-02-12","cited_by_count":29,"area":"diffusion"},{"id":"2506.16471","title":"Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities","created":"2025-06-19","cited_by_count":26,"area":"diffusion"},{"id":"2512.21829","title":"Tilt Matching for Scalable Sampling and Fine-Tuning","created":"2025-12-26","cited_by_count":22,"area":"diffusion"},{"id":"2506.22565","title":"Adjoint Schr\\\"odinger Bridge Sampler","created":"2025-06-27","cited_by_count":21,"area":"diffusion"},{"id":"2508.10684","title":"MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control","created":"2025-08-14","cited_by_count":21,"area":"diffusion"},{"id":"2502.04778","title":"Behavior-Regularized Diffusion Policy Optimization for Offline Reinforcement Learning","created":"2025-02-07","cited_by_count":20,"area":"diffusion"},{"id":"2501.06148","title":"From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training","created":"2025-01-10","cited_by_count":19,"area":"diffusion"},{"id":"2502.06999","title":"Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models","created":"2025-02-10","cited_by_count":17,"area":"diffusion"},{"id":"2406.10808","title":"Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching","created":"2024-06-16","cited_by_count":13,"area":"diffusion"},{"id":"2408.16249","title":"Iterated Energy-based Flow Matching for Sampling from Boltzmann Densities","created":"2024-08-29","cited_by_count":13,"area":"diffusion"},{"id":"2502.16824","title":"Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization","created":"2025-02-24","cited_by_count":13,"area":"diffusion"},{"id":"2506.05231","title":"Progressive Tempering Sampler with Diffusion","created":"2025-06-05","cited_by_count":13,"area":"diffusion"},{"id":"2601.08136","title":"Reverse Flow Matching: A Unified Framework for Online Reinforcement Learning with Diffusion and Flow Policies","created":"2026-01-13","cited_by_count":12,"area":"diffusion"},{"id":"2509.23265","title":"CREPE: Controlling Diffusion with Replica Exchange","created":"2025-09-27","cited_by_count":11,"area":"diffusion"},{"id":"2505.19552","title":"On scalable and efficient training of diffusion samplers","created":"2025-05-26","cited_by_count":10,"area":"diffusion"},{"id":"2603.00530","title":"Bridge Matching Sampler: Scalable Sampling via Generalized Fixed-Point Diffusion Matching","created":"2026-02-28","cited_by_count":10,"area":"diffusion"},{"id":"2405.14392","title":"Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows","created":"2024-05-23","cited_by_count":9,"area":"diffusion"},{"id":"2410.19449","title":"Learned Reference-based Diffusion Sampling for multi-modal distributions","created":"2024-10-25","cited_by_count":9,"area":"diffusion"},{"id":"2506.18165","title":"Non-equilibrium Annealed Adjoint Sampler","created":"2025-06-22","cited_by_count":8,"area":"diffusion"},{"id":"2410.20470","title":"Hamiltonian Score Matching and Generative Flows","created":"2024-10-27","cited_by_count":7,"area":"diffusion"},{"id":"2509.03726","title":"Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling","created":"2025-09-03","cited_by_count":7,"area":"diffusion"},{"id":"2502.04468","title":"Iterative Importance Fine-tuning of Diffusion Models","created":"2025-02-06","cited_by_count":6,"area":"diffusion"},{"id":"2505.21005","title":"Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models","created":"2025-05-27","cited_by_count":6,"area":"diffusion"},{"id":"2506.01541","title":"Adaptive Destruction Processes for Diffusion Samplers","created":"2025-06-02","cited_by_count":6,"area":"diffusion"},{"id":"2512.20003","title":"Control Variate Score Matching for Diffusion Models","created":"2025-12-23","cited_by_count":6,"area":"diffusion"},{"id":"2406.14040","title":"A Practical Diffusion Path for Sampling","created":"2024-06-20","cited_by_count":5,"area":"diffusion"},{"id":"2602.02924","title":"How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models?","created":"2026-02-02","cited_by_count":5,"area":"diffusion"},{"id":"2605.03984","title":"Flow Sampling: Learning to Sample from Unnormalized Densities via Denoising Conditional Processes","created":"2026-05-05","cited_by_count":5,"area":"diffusion"},{"id":"2606.22630","title":"Scalable Maximum Entropy Reinforcement Learning for Diffusion Policies via Adjoint Matching","created":"2026-06-21","cited_by_count":5,"area":"diffusion"},{"id":"2503.00524","title":"End-To-End Learning of Gaussian Mixture Priors for Diffusion Sampler","created":"2025-03-01","cited_by_count":4,"area":"diffusion"},{"id":"2603.27450","title":"FlowRL: A Taxonomy and Modular Framework for Reinforcement Learning with Diffusion Policies","created":"2026-03-29","cited_by_count":3,"area":"diffusion"},{"id":"2505.19431","title":"Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage","created":"2025-05-26","cited_by_count":2,"area":"diffusion"},{"id":"2601.15417","title":"Ambient Dataloops: Generative Models for Dataset Refinement","created":"2026-01-21","cited_by_count":2,"area":"diffusion"},{"id":"2602.00357","title":"Planning with Language and Generative Models: Toward General Reward-Guided Wireless Network Design","created":"2026-01-30","cited_by_count":2,"area":"diffusion"},{"id":"2602.05961","title":"Discrete diffusion samplers and bridges: Off-policy algorithms and applications in latent spaces","created":"2026-02-05","cited_by_count":2,"area":"diffusion"},{"id":"2602.08267","title":"Inverting Data Transformations via Diffusion Sampling","created":"2026-02-09","cited_by_count":2,"area":"diffusion"},{"id":"2605.23365","title":"Score-Based One-step MeanFlow Policy Optimization","created":"2026-05-22","cited_by_count":2,"area":"diffusion"},{"id":"2606.17192","title":"Constrained Diffusion Models with Primal-Dual Inference","created":"2026-06-15","cited_by_count":2,"area":"diffusion"},{"id":"2609.00279","title":"Exact Global MCMC with Denoising Diffusion","created":"2026-08-31","cited_by_count":2,"area":"diffusion"},{"id":"2512.05251","title":"One-Step Diffusion Samplers via Self-Distillation and Deterministic Flow","created":"2025-12-04","cited_by_count":1,"area":"diffusion"},{"id":"2603.10250","title":"GeMPO: Generalized Measure Matching for Online Diffusion Reinforcement Learning","created":"2026-03-10","cited_by_count":1,"area":"diffusion"},{"id":"2605.04013","title":"Conditional Diffusion Sampling","created":"2026-05-05","cited_by_count":1,"area":"diffusion"},{"id":"2503.14569","title":"Potential Score Matching: Debiasing Molecular Structure Sampling with Potential Energy Guidance","created":"2025-03-18","cited_by_count":0,"area":"diffusion"},{"id":"2601.01594","title":"Variance-Reduced Diffusion Sampling via Target Score Identity","created":"2026-01-04","cited_by_count":0,"area":"diffusion"},{"id":"2605.00337","title":"FES-FM: Free Energy Surface Sampling via Reduced Flow Matching","created":"2026-05-01","cited_by_count":0,"area":"diffusion"},{"id":"2610.01759","title":"PhysDEM: Physics-Defined Energy-Matching Diffusion for Spatiotemporal Field Generation under Scarce Measurements","created":"2026-10-01","cited_by_count":0,"area":"diffusion"},{"id":"2610.04696","title":"Score-Calibrated Flow for Sampling from Unnormalized Densities with Applications to Generative Online Reinforcement Learning","created":"2026-10-03","cited_by_count":0,"area":"diffusion"},{"id":"2610.12848","title":"Reinforcement Learning via Fixed-point Flow Matching","created":"2026-10-09","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}