{"total":147,"rows":[{"id":"2409.08861","title":"Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control","created":"2024-09-13","cited_by_count":233,"area":"diffusion"},{"id":"2402.15194","title":"Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control","created":"2024-02-23","cited_by_count":143,"area":"diffusion"},{"id":"2402.06121","title":"Iterated Denoising Energy Matching for Sampling from Boltzmann Densities","created":"2024-02-09","cited_by_count":116,"area":null},{"id":"2307.01198","title":"Improved sampling via learned diffusions","created":"2023-07-03","cited_by_count":104,"area":null},{"id":"2501.05803","title":"Test-time Alignment of Diffusion Models without Reward Over-optimization","created":"2025-01-10","cited_by_count":93,"area":"diffusion"},{"id":"2403.14404","title":"Physics-Informed Diffusion Models","created":"2024-03-21","cited_by_count":90,"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":"2305.19693","title":"Spontaneous Symmetry Breaking in Generative Diffusion Models","created":"2023-05-31","cited_by_count":80,"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":"2410.02711","title":"NETS: A Non-Equilibrium Transport Sampler","created":"2024-10-03","cited_by_count":69,"area":null},{"id":"2402.06320","title":"Particle Denoising Diffusion Sampler","created":"2024-02-09","cited_by_count":65,"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":"2310.02679","title":"Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization","created":"2023-10-04","cited_by_count":57,"area":null},{"id":"2406.07423","title":"Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling","created":"2024-06-11","cited_by_count":56,"area":null},{"id":"2402.14285","title":"Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion","created":"2024-02-22","cited_by_count":54,"area":"diffusion"},{"id":"2509.01025","title":"Any-Order Flexible Length Masked Diffusion","created":"2025-08-31","cited_by_count":51,"area":"diffusion"},{"id":"2402.05098","title":"Improved off-policy training of diffusion samplers","created":"2024-02-07","cited_by_count":48,"area":"diffusion"},{"id":"2412.07081","title":"Sequential Controlled Langevin Diffusions","created":"2024-12-10","cited_by_count":48,"area":null},{"id":"2406.01781","title":"DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised $h$-transform","created":"2024-06-03","cited_by_count":46,"area":"diffusion"},{"id":"2307.02037","title":"Reverse Diffusion Monte Carlo","created":"2023-07-05","cited_by_count":41,"area":null},{"id":"2312.02027","title":"Stochastic Optimal Control Matching","created":"2023-12-04","cited_by_count":40,"area":null},{"id":"2502.18462","title":"Scalable Equilibrium Sampling with Sequential Boltzmann Generators","created":"2025-02-25","cited_by_count":38,"area":null},{"id":"2407.01573","title":"Model-Based Diffusion for Trajectory Optimization","created":"2024-05-28","cited_by_count":32,"area":"diffusion"},{"id":"2501.02298","title":"Beyond Log-Concavity and Score Regularity: Improved Convergence Bounds for Score-Based Generative Models in W2-distance","created":"2025-01-04","cited_by_count":29,"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":"2503.01006","title":"Underdamped Diffusion Bridges with Applications to Sampling","created":"2025-03-02","cited_by_count":29,"area":null},{"id":"2304.13534","title":"A mean-field games laboratory for generative modeling","created":"2023-04-26","cited_by_count":28,"area":null},{"id":"2402.10758","title":"Stochastic Localization via Iterative Posterior Sampling","created":"2024-02-16","cited_by_count":28,"area":null},{"id":"2502.06685","title":"No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers","created":"2025-02-10","cited_by_count":28,"area":null},{"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":"2508.12511","title":"Trust Region Constrained Measure Transport in Path Space for Stochastic Optimal Control and Inference","created":"2025-08-17","cited_by_count":23,"area":null},{"id":"2502.03686","title":"Variational Control for Guidance in Diffusion Models","created":"2025-02-06","cited_by_count":22,"area":"diffusion"},{"id":"2307.01050","title":"Transport meets Variational Inference: Controlled Monte Carlo Diffusions","created":"2023-07-03","cited_by_count":21,"area":null},{"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":"2407.07873","title":"Dynamical Measure Transport and Neural PDE Solvers for Sampling","created":"2024-07-10","cited_by_count":20,"area":null},{"id":"2502.05749","title":"UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control","created":"2025-02-09","cited_by_count":20,"area":null},{"id":"2412.18164","title":"Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence","created":"2024-12-24","cited_by_count":19,"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":"2410.02217","title":"Stochastic Sampling from Deterministic Flow Models","created":"2024-10-03","cited_by_count":18,"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":"2502.10328","title":"Accelerated Parallel Tempering via Neural Transports","created":"2025-02-14","cited_by_count":17,"area":null},{"id":"2405.20630","title":"Stochastic Optimal Control for Diffusion Bridges in Function Spaces","created":"2024-05-31","cited_by_count":16,"area":"diffusion"},{"id":"2407.09645","title":"Hamilton-Jacobi Reachability in Reinforcement Learning: A Survey","created":"2024-07-12","cited_by_count":16,"area":"deeprl"},{"id":"2211.06757","title":"DriftRec: Adapting diffusion models to blind JPEG restoration","created":"2022-11-12","cited_by_count":15,"area":"diffusion"},{"id":"2409.09787","title":"BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching","created":"2024-09-15","cited_by_count":15,"area":null},{"id":"2501.19077","title":"Temperature-Annealed Boltzmann Generators","created":"2025-01-31","cited_by_count":15,"area":null},{"id":"2504.03461","title":"Conditioning Diffusions Using Malliavin Calculus","created":"2025-04-04","cited_by_count":15,"area":null},{"id":"2512.05116","title":"Value Gradient Guidance for Flow Matching Alignment","created":"2025-12-04","cited_by_count":14,"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":"2505.17741","title":"Discrete Neural Flow Samplers with Locally Equivariant Transformer","created":"2025-05-23","cited_by_count":13,"area":null},{"id":"2508.18175","title":"Amortized Sampling with Transferable Normalizing Flows","created":"2025-08-25","cited_by_count":12,"area":null},{"id":"2511.08425","title":"HardFlow: Hard-Constrained Sampling for Flow-Matching Models via Trajectory Optimization","created":"2025-11-11","cited_by_count":11,"area":"diffusion"},{"id":"2410.05602","title":"Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series","created":"2024-10-08","cited_by_count":10,"area":null},{"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":"2301.13636","title":"Transport with Support: Data-Conditional Diffusion Bridges","created":"2023-01-31","cited_by_count":9,"area":null},{"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":"2504.08916","title":"Improving the evaluation of samplers on multi-modal targets","created":"2025-04-11","cited_by_count":9,"area":null},{"id":"2403.01717","title":"Soft-constrained Schrodinger Bridge: a Stochastic Control Approach","created":"2024-03-04","cited_by_count":8,"area":null},{"id":"2405.03549","title":"Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models","created":"2024-05-06","cited_by_count":8,"area":"diffusion"},{"id":"2412.16748","title":"Solving Inverse Problems via Diffusion Optimal Control","created":"2024-12-21","cited_by_count":8,"area":null},{"id":"2602.16796","title":"Efficient Tail-Aware Generative Optimization via Flow Model Fine-Tuning","created":"2026-02-18","cited_by_count":8,"area":null},{"id":"2401.02080","title":"Energy based diffusion generator for efficient sampling of Boltzmann distributions","created":"2024-01-04","cited_by_count":7,"area":null},{"id":"2401.14029","title":"Towards a Systems Theory of Algorithms","created":"2024-01-25","cited_by_count":7,"area":null},{"id":"2409.03817","title":"Neural Entropy","created":"2024-09-05","cited_by_count":7,"area":"diffusion"},{"id":"2410.15336","title":"Diffusion-PINN Sampler","created":"2024-10-20","cited_by_count":7,"area":"diffusion"},{"id":"2506.10982","title":"Rethinking Losses for Diffusion Bridge Samplers","created":"2025-06-12","cited_by_count":7,"area":null},{"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":"2506.01541","title":"Adaptive Destruction Processes for Diffusion Samplers","created":"2025-06-02","cited_by_count":6,"area":"diffusion"},{"id":"2509.26364","title":"Data-to-Energy Stochastic Dynamics","created":"2025-09-30","cited_by_count":6,"area":"diffusion"},{"id":"2510.11711","title":"Reinforced sequential Monte Carlo for amortised sampling","created":"2025-10-13","cited_by_count":6,"area":null},{"id":"2512.20003","title":"Control Variate Score Matching for Diffusion Models","created":"2025-12-23","cited_by_count":6,"area":"diffusion"},{"id":"2604.08580","title":"Adjoint Matching through the Lens of the Stochastic Maximum Principle in Optimal Control","created":"2026-03-28","cited_by_count":6,"area":null},{"id":"2310.04490","title":"Generative Diffusion From An Action Principle","created":"2023-10-06","cited_by_count":5,"area":"diffusion"},{"id":"2502.07337","title":"Neural Flow Samplers with Shortcut Models","created":"2025-02-11","cited_by_count":5,"area":null},{"id":"2507.00480","title":"Posterior Inference in Latent Space for Scalable Constrained Black-box Optimization","created":"2025-07-01","cited_by_count":5,"area":null},{"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":"2605.06583","title":"Improved techniques for fine-tuning flow models via adjoint matching: a deterministic control pipeline","created":"2026-05-07","cited_by_count":5,"area":"alignment"},{"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":"2407.00626","title":"Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models","created":"2024-06-30","cited_by_count":4,"area":"diffusion"},{"id":"2410.03368","title":"Latent Abstractions in Generative Diffusion Models","created":"2024-10-04","cited_by_count":4,"area":"diffusion"},{"id":"2410.07550","title":"Conditional Lagrangian Wasserstein Flow for Time Series Imputation","created":"2024-10-10","cited_by_count":4,"area":null},{"id":"2411.05899","title":"Streaming Bayes GFlowNets","created":"2024-11-08","cited_by_count":4,"area":null},{"id":"2501.19373","title":"Beyond Fixed Horizons: A Theoretical Framework for Adaptive Denoising Diffusions","created":"2025-01-31","cited_by_count":4,"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":"2511.06239","title":"Functional Adjoint Sampler: Scalable Sampling on Infinite Dimensional Spaces","created":"2025-11-09","cited_by_count":4,"area":null},{"id":"2402.15285","title":"Generative Modelling with Tensor Train approximations of Hamilton--Jacobi--Bellman equations","created":"2024-02-23","cited_by_count":3,"area":null},{"id":"2405.15625","title":"Nonlinear denoising score matching for enhanced learning of structured distributions","created":"2024-05-24","cited_by_count":3,"area":"diffusion"},{"id":"2410.10118","title":"Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning","created":"2024-10-14","cited_by_count":3,"area":null},{"id":"2505.19646","title":"Energy-based generator matching: A neural sampler for general state space","created":"2025-05-26","cited_by_count":3,"area":null},{"id":"2511.01795","title":"Fractional Diffusion Bridge Models","created":"2025-11-03","cited_by_count":3,"area":null},{"id":"2512.02019","title":"Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning","created":"2025-12-01","cited_by_count":3,"area":"diffusion"},{"id":"2401.02739","title":"Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors","created":"2024-01-05","cited_by_count":2,"area":"diffusion"},{"id":"2409.09614","title":"HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative models","created":"2024-09-15","cited_by_count":2,"area":"diffusion"},{"id":"2504.20940","title":"Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Data","created":"2025-04-29","cited_by_count":2,"area":null},{"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"}],"next_cursor":"100"}