{"total":34,"rows":[{"id":"2502.00234","title":"Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms","created":"2025-02-01","cited_by_count":47,"area":"diffusion"},{"id":"2410.18784","title":"Denoising diffusion probabilistic models are optimally adaptive to unknown low dimensionality","created":"2024-10-24","cited_by_count":44,"area":"diffusion"},{"id":"2501.12982","title":"Low-dimensional adaptation of diffusion models: Convergence in total variation","created":"2025-01-22","cited_by_count":39,"area":"diffusion"},{"id":"2410.24060","title":"Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure","created":"2024-10-31","cited_by_count":38,"area":"diffusion"},{"id":"2410.23285","title":"Provable Acceleration for Diffusion Models under Minimal Assumptions","created":"2024-10-30","cited_by_count":27,"area":"diffusion"},{"id":"2503.09583","title":"Minimax Optimality of the Probability Flow ODE for Diffusion Models","created":"2025-03-12","cited_by_count":23,"area":"diffusion"},{"id":"2504.05300","title":"Dimension-Free Convergence of Diffusion Models for Approximate Gaussian Mixtures","created":"2025-04-07","cited_by_count":23,"area":"diffusion"},{"id":"2411.17522","title":"On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality","created":"2024-11-26","cited_by_count":22,"area":"diffusion"},{"id":"2506.03979","title":"Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach","created":"2025-06-04","cited_by_count":18,"area":"diffusion"},{"id":"2502.04849","title":"Advancing Wasserstein Convergence Analysis of Score-Based Models: Insights from Discretization and Second-Order Acceleration","created":"2025-02-07","cited_by_count":17,"area":"diffusion"},{"id":"2509.01629","title":"Lipschitz-Guided Design of Interpolation Schedules in Generative Models","created":"2025-09-01","cited_by_count":17,"area":"diffusion"},{"id":"2506.24042","title":"Faster Diffusion Models via Higher-Order Approximation","created":"2025-06-30","cited_by_count":16,"area":"diffusion"},{"id":"2510.27562","title":"Optimal Convergence Analysis of DDPM for General Distributions","created":"2025-10-31","cited_by_count":16,"area":"diffusion"},{"id":"2602.05533","title":"Conditional Diffusion Guidance under Hard Constraint: A Stochastic Analysis Approach","created":"2026-02-05","cited_by_count":11,"area":"diffusion"},{"id":"2506.09681","title":"Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds","created":"2025-06-11","cited_by_count":9,"area":"diffusion"},{"id":"2506.13061","title":"Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models","created":"2025-06-16","cited_by_count":9,"area":"diffusion"},{"id":"2512.04985","title":"Towards a unified framework for guided diffusion models","created":"2025-12-04","cited_by_count":8,"area":"diffusion"},{"id":"2511.04844","title":"Sublinear iterations can suffice even for DDPMs","created":"2025-11-06","cited_by_count":7,"area":"diffusion"},{"id":"2601.10708","title":"High-accuracy and dimension-free sampling with diffusions","created":"2026-01-15","cited_by_count":7,"area":"diffusion"},{"id":"2502.02305","title":"Information-Theoretic Proofs for Diffusion Sampling","created":"2025-02-04","cited_by_count":6,"area":"diffusion"},{"id":"2601.06715","title":"Diffusion Models with Heavy-Tailed Targets: Score Estimation and Sampling Guarantees","created":"2026-01-10","cited_by_count":5,"area":"diffusion"},{"id":"2601.18681","title":"ART for Diffusion Sampling: A Reinforcement Learning Approach to Timestep Schedule","created":"2026-01-26","cited_by_count":4,"area":"diffusion"},{"id":"2507.08956","title":"Beyond Scores: Proximal Diffusion Models","created":"2025-07-11","cited_by_count":3,"area":"diffusion"},{"id":"2604.10857","title":"Query Lower Bounds for Diffusion Sampling","created":"2026-04-12","cited_by_count":2,"area":"diffusion"},{"id":"2605.30332","title":"Colored Noise Diffusion Sampling","created":"2026-05-28","cited_by_count":2,"area":"diffusion"},{"id":"2511.18742","title":"ProxT2I: Efficient Reward-Guided Text-to-Image Generation via Proximal Diffusion","created":"2025-11-24","cited_by_count":1,"area":"diffusion"},{"id":"2601.21200","title":"Provably Reliable Classifier Guidance via Cross-Entropy Control","created":"2026-01-29","cited_by_count":1,"area":"diffusion"},{"id":"2605.07220","title":"On the Robustness of Distribution Support under Diffusion Guidance","created":"2026-05-08","cited_by_count":1,"area":"diffusion"},{"id":"2605.27352","title":"From Scores to Gibbs Correctors: Accelerating Uniform-Rate Discrete Diffusion Models","created":"2026-05-26","cited_by_count":1,"area":"diffusion"},{"id":"2606.06007","title":"Diffusion Models for Adaptive Sequential Data Generation","created":"2026-06-04","cited_by_count":1,"area":"diffusion"},{"id":"2606.23627","title":"Diffusion Models Adapt to Low-Dimensional Structure Under Flexible Coefficient Choices","created":"2026-06-22","cited_by_count":1,"area":"diffusion"},{"id":"2512.24927","title":"Are First-Order Diffusion Samplers Really Slower? A Fast Forward-Value Approach","created":"2025-12-31","cited_by_count":0,"area":"diffusion"},{"id":"2606.13796","title":"Recursively Trained Diffusion Models: Limiting Collapse Distribution and Spectral Characterization","created":"2026-06-11","cited_by_count":0,"area":"diffusion"},{"id":"2607.02137","title":"ART for Diffusion Sampling: Continuous-Time Control and Actor-Critic Learning","created":"2026-07-02","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}